In [1]:
%matplotlib inline
Import custom scripts¶
In [2]:
path_scripts = '/mnt/kauffman/joosts/projects/STRT_epidermis/scripts'
In [3]:
import sys
sys.path.append(path_scripts)
In [4]:
from EPI_misc_scripts_v1_1 import *
from EPI_affinity_propagation_v1_0 import *
from EPI_neg_binom_regression_v1_1 import *
from EPI_pseudotemporal_ordering_v1_0 import *
In [5]:
import matplotlib as mpl
Initialize engines¶
In [6]:
from ipyparallel import Client
In [7]:
c = Client(profile='default')
In [8]:
dview = c[:]
In [9]:
dview.execute('import sys')
dview.execute('sys.path.append("/mnt/kauffman/joosts/projects/STRT_epidermis/scripts")')
dview.execute('from EPI_misc_scripts_v1_1 import *')
dview.execute('from EPI_affinity_propagation_v1_0 import *')
dview.execute('from EPI_neg_binom_regression_v1_1 import *')
dview.execute('from EPI_pseudotemporal_ordering_v1_0 import *')
Out[9]:
Import Data¶
In [10]:
exp_id = '201509151726'
path_input = '/mnt/kauffman/joosts/projects/STRT_epidermis/data_input/v1.8'
path_output = '/mnt/kauffman/joosts/projects/STRT_epidermis/data_output/v1.8'
path_figures = '/mnt/kauffman/joosts/projects/STRT_epidermis/figures/v1.8'
In [11]:
seq = loadData_v1(path_input, exp_id, 'seq', 'DataFrame')
meta = loadData_v1(path_input, exp_id, 'meta', 'DataFrame')
In [12]:
s_groups_1st = loadData_v1(path_output, exp_id, 's_groups_1st', 'Series')
g_groups_1st = loadData_v1(path_output, exp_id, 'g_groups_1st', 'Series')
In [13]:
tsne_1st = loadData_v1(path_output, exp_id, 'tsne_1st', 'DataFrame')
In [14]:
s_groups_IFE_b = loadData_v1(path_output, exp_id, 's_groups_IFE_b', 'Series')
g_groups_IFE_b = loadData_v1(path_output, exp_id, 'g_groups_IFE_b', 'Series')
In [15]:
s_groups_uHF = loadData_v1(path_output, exp_id, 's_groups_uHF', 'Series')
g_groups_uHF = loadData_v1(path_output, exp_id, 'g_groups_uHF', 'Series')
In [16]:
s_groups_OB = loadData_v1(path_output, exp_id, 's_groups_OB', 'Series')
g_groups_OB = loadData_v1(path_output, exp_id, 'g_groups_OB', 'Series')
In [17]:
s_groups_IB = loadData_v1(path_output, exp_id, 's_groups_IB', 'Series')
g_groups_IB = loadData_v1(path_output, exp_id, 'g_groups_IB', 'Series')
In [18]:
s_groups_2nd = loadData_v1(path_output, exp_id, 's_groups_2nd', 'Series')
In [19]:
NBR_2nd_traces = loadData_from_pickle_v1(path_output, exp_id, 'NBR_2nd_traces')
NBR_2nd_summary = loadData_from_pickle_v1(path_output, exp_id,'NBR_2nd_summary')
NBR_2nd_bin_bl = loadData_from_pickle_v1(path_output,exp_id,'NBR_2nd_bin_bl')
NBR_2nd_size_bl = loadData_from_pickle_v1(path_output,exp_id,'NBR_2nd_size_bl')
NBR_2nd_bin_gr = loadData_from_pickle_v1(path_output,exp_id,'NBR_2nd_bin_gr')
NBR_2nd_size_gr = loadData_from_pickle_v1(path_output,exp_id,'NBR_2nd_size_gr')
In [20]:
robustness_IFE_b = loadData_v1(path_output, exp_id, 'robustness_IFE_b', 'DataFrame')
robustness_IFE_b_null = loadData_v1(path_output, exp_id, 'robustness_IFE_b_null', 'DataFrame')
robustness_uHF = loadData_v1(path_output, exp_id, 'robustness_uHF', 'DataFrame')
robustness_uHF_null = loadData_v1(path_output, exp_id, 'robustness_uHF_null', 'DataFrame')
robustness_OB = loadData_v1(path_output, exp_id, 'robustness_OB', 'DataFrame')
robustness_OB_null = loadData_v1(path_output, exp_id, 'robustness_OB_null', 'DataFrame')
robustness_IB = loadData_v1(path_output, exp_id, 'robustness_IB', 'DataFrame')
robustness_IB_null = loadData_v1(path_output, exp_id, 'robustness_IB_null', 'DataFrame')
Colormaps¶
In [21]:
cmap_1st = {2:'#00CC00',
1:'#FFE000',
8:'#FF9900',
0:'#FF3300',
10:'#CC0000',
6:'#cab2d6',
3:'#A68BC2',
4:'#65429A',
5:'#000066',
7:'#0066FF',
9:'#33CCCC',
11:'#FF99CC',
12:'#660033'}
In [22]:
#Create colormap and colorlist for hair cycle stage data
cmap_hair_cycle = {'mid telogen (Ki67)': '#00CC00', 'late telogen (Ki67)': '#FFCC00'}
In [23]:
#Create colormap and colorlist for Sca1 data
cmap_Sca1 = {'1': '#FF0000', '0': '#0066FF'}
In [24]:
cmap_IFE_b = {2: '#33a02c',
1: '#b2df8a',
0: '#00FF00'}
In [25]:
cmap_uHF = {6:'#CC0066',
2:'#FF99CC',
3:'#FFCCCC',
1:'#D2C5E1',
5:'#A68BC2',
4:'#6a3d9a',
0:'#2A183E'}
In [26]:
cmap_OB = {1:'#0000FF',
0:'#33CCFF',
4:'#99CCFF',
3:'#666699',
2:'#000066'}
In [27]:
cmap_IB = {2:'#33CCCC',
1:'#00FFFF',
0:'#006666'}
In [28]:
cmap_2nd = {0: '#33a02c',
1: '#b2df8a',
2: '#00FF00',
3:'#FFE000',
4:'#FF9900',
5:'#FF3300',
6:'#CC0000',
7:'#CC0066',
8:'#FF99CC',
9:'#FFCCCC',
10:'#D2C5E1',
11:'#A68BC2',
12:'#6a3d9a',
13:'#2A183E',
14:'#000066',
15:'#0000FF',
16:'#33CCFF',
17:'#99CCFF',
18:'#666699',
19:'#000066',
20:'#33CCCC',
21:'#00FFFF',
22:'#006666',
23:'#FF99CC',
24:'#660033'}
In [29]:
markers_2nd = {0: 'o',
1: 'o',
2: 'o',
3:'s',
4:'s',
5:'s',
6:'s',
7:'^',
8:'^',
9:'^',
10:'^',
11:'^',
12:'^',
13:'^',
14:'s',
15:'D',
16:'D',
17:'D',
18:'D',
19:'D',
20:'H',
21:'H',
22:'H',
23:'s',
24:'s'}
In [30]:
markers_2nd_size = {0:750,
1:750,
2:750,
3:750,
4:750,
5:750,
6:750,
7:750,
8:750,
9:750,
10:750,
11:750,
12:750,
13:750,
14:750,
15:500,
16:500,
17:500,
18:500,
19:500,
20:750,
21:750,
22:750,
23:750,
24:750}
Group identity in subclusterings (s_groups_XY) vs. fused group file (s_groups_2nd)¶
In [31]:
IFE_b_to_2nd = {2:0,
1:1,
0:2}
In [32]:
uHF_to_2nd = {6:7,
2:8,
3:9,
1:10,
5:11,
4:12,
0:13}
In [33]:
OB_to_2nd = {1:15,
0:16,
4:17,
3:18,
2:19}
In [34]:
IB_to_2nd = {2:20,
1:21,
0:22}
Name maps¶
In [35]:
nmap_IFE_b = {2:'Interfollicular basal I',
1:'Interfollicular basal II',
0:'Infundibular basal'}
In [36]:
gmap_IFE_b = {2:'Krt14(hi), Krt5(hi), Mt2(hi), Fth1(hi),\nThbs1(hi)',
1:'Krt14(hi), Krt5(hi), Mt2(hi), Fth1(hi),\nThbs1(lo)',
0:'Krt14(hi), Krt5(hi), Mt2(hi), Fth1(hi),\nSostdc1(hi), Aqp3(hi), Fst(hi), Postn(dim)'}
In [37]:
nmap_uHF = {6:'upper HF I',
2:'upper HF II',
3:'upper HF III',
1:'upper HF IV',
5:'upper HF V',
4:'upper HF VI',
0:'upper HF VII'}
In [38]:
gmap_uHF = {6:'Krt79(hi), Krt17(hi),\nDefb6(hi), Cst6(hi),\nKlk10(hi), Cryab(hi)',
2:'Krt79(hi), Krt17(hi),\nDefb6(hi), Cst6(dim)\nKrt14(hi), Lrig1(+)\nKrt5(lo), Ptn(lo)',
3:'Krt79(hi), Krt17(hi),\nDefb6(lo), Cst6(lo),\nKrt14(hi), Lrig1(+)\nKrt5(lo), Ptn(lo)',
1:'Krt79(lo), Krt17(lo),\nKrt14(hi), Lrig1(+)\nKrt5(hi), Ptn(dim)',
5:'Krt79(dim), Krt17(dim),\nKrt14(dim), Krt5(dim),\nKrt10(dim), Ptgs1(dim)',
4:'Krt79(hi), Krt17(hi),\nKrt10(hi), Ptgs1(hi)',
0:'Krt79(hi), Krt17(hi),\nLor(hi), Flg2(hi)'}
In [39]:
nmap_OB = {1:'Outer bulge I',
0:'Outer bulge II',
4:'Outer bulge III',
3:'Outer bulge IV',
2:'Outer bulge V'}
In [40]:
gmap_OB = {1:'Postn(hi), Cd34(hi),\nLgr5(hi), Krt24(hi),\nSfrp1(hi)',
0:'Postn(hi), Cd34(hi),\nLgr5(lo), Krt24(lo),\nSfrp1(lo)',
4:'Postn(hi), Cd34(hi),\nAspn(hi), Nrep(hi),\nKrt17(hi), Krt15(lo)',
3:'Postn(hi), Cd34(hi),\nKrt79(hi), Krt17(hi)',
2:'Postn(hi), Cd34(hi),\nKrt10(hi), Ptgs1(hi)'}
In [41]:
nmap_IB = {2:'Inner bulge I',
1:'Inner bulge II',
0:'Inner bulge III'}
In [42]:
gmap_IB = {2:'Krt6a(hi), Krt75(hi)',
1:'Krt6a(hi), Krt75(hi),\nPostn(hi), Cd34(hi)',
0:'Krt6a(hi), Krt75(hi),\nKrt10(hi), Ptgs1(hi)'}
In [43]:
nmap_2nd_short = {0:'IFE B I',
1:'IFE B II',
2:'INFU B',
3:'IFE D I',
4:'IFE D II',
5:'IFE K I',
6:'IFE K II',
7:'uHF I',
8:'uHF II',
9:'uHF III',
10:'uHF IV',
11:'uHF V',
12:'uHF VI',
13:'uHF VII',
14:'SG',
15:'OB I',
16:'OB II',
17:'OB III',
18:'OB IV',
19:'OB V',
20:'IB I',
21:'IB II',
22:'IB III',
23:'TC',
24:'LH'}
IFE basal cells¶
t-SNE¶
In [305]:
tsne = tsne_1st
cell_groups = s_groups_IFE_b
cmap = cmap_IFE_b
#initialize figure
height = 15
width = 15
fig = plt.figure(facecolor = 'w', figsize = (width, height))
#define x- and y-limits
x_min, x_max = np.min(tsne['x']), np.max(tsne['x'])
y_min, y_max = np.min(tsne['y']), np.max(tsne['y'])
x_diff, y_diff = x_max - x_min, y_max - y_min
pad = 2.0
if x_diff > y_diff:
xlim = (x_min - 1, x_max + 3)
ylim = (y_min * (x_diff / y_diff) - pad, y_max * (x_diff / y_diff) + pad)
if x_diff < y_diff:
xlim = (x_min * (y_diff/x_diff) - 1, x_max * (y_diff/x_diff) + 3)
ylim = (y_min - pad, y_max + pad)
text_pad = 2
ix_sel = cell_groups.index
#draw groups in background
ax1 = plt.subplot()
ax1.set_xlim(xlim)
ax1.set_ylim(ylim)
remove_ticks(ax1)
ax1.scatter(tsne['x'],
tsne['y'],
s = 150,
linewidth = 0.0,
c = 'silver',
edgecolor = 'silver')
clist_tsne = [cmap[cell_groups[ix]] for ix in ix_sel]
ax1.scatter(tsne.ix[ix_sel, 'x'],
tsne.ix[ix_sel, 'y'],
s = 150,
linewidth = 0.0,
c = clist_tsne,
edgecolors = clist_tsne)
clean_axis(ax1)
figname = 'v1.8_2_A_t-SNE_IFE_b.pdf'
plt.savefig('%s/%s' % (path_figures, figname),
format = 'pdf',
transparent = True,
bbox_inches = 'tight',
pad_inches = 0,
rasterized = True)
In [306]:
#initialize figure
height = 5
width = 5
fig = plt.figure(facecolor = 'w', figsize = (width, height))
#draw inset
ax_inset = plt.subplot()
remove_ticks(ax_inset)
clist_tsne_inset = [cmap_1st[s_groups_1st[ix]] for ix in ix_sel]
ax_inset.scatter(tsne['x'],
tsne['y'],
s = 25,
linewidth = 0.0,
c = 'silver',
cmap = plt.cm.jet,
vmin = 0, vmax = 1.0)
ax_inset.scatter(tsne.ix[ix_sel, 'x'],
tsne.ix[ix_sel, 'y'],
s = 25,
linewidth = 0.0,
c = clist_tsne_inset,
edgecolor = clist_tsne_inset)
for s in ['top','bottom','left','right']:
ax_inset.spines[s].set_linewidth(1)
figname = 'v1.8_S3_A_t-SNE_IFE_b_Inset.pdf'
plt.savefig('%s/%s' % (path_figures, figname),
format = 'pdf',
transparent = True,
bbox_inches = 'tight',
pad_inches = 0,
rasterized = True)
Heatmap¶
In [45]:
#Transfer the expression data to relative values (0 [min] - 1 [max])
seq_sel_IFE_b = log2Transform(seq).ix[g_groups_IFE_b.index, s_groups_IFE_b.index]
seq_sel_IFE_b = seq_sel_IFE_b.apply(lambda x: x / max(x), axis = 1)
In [262]:
dataset = seq_sel_IFE_b
cell_groups = s_groups_IFE_b
gene_groups = g_groups_IFE_b
cmap = cmap_IFE_b
#create figure
bar_size = 0.75 #size of group and metadata bars
bar_pad = 0.1 #pad size of group and metadata bars
width = 0.04 * len(dataset.columns)
height = 32.0
#height = ((width - bar_size) / len(s_groups)) * len(g_groups) + 5 * bar_size
plt.figure(figsize=(width, height), facecolor = 'w')
#generate main axes
axHM = plt.axes()
axHM.set_position([0.00, 0.00, 1.0, 1.0])
#plot heatmap
axHM.matshow(dataset,
cmap = plt.cm.jet,
aspect = 'auto',
interpolation = 'nearest',
vmin = 0,
vmax = 1)
remove_ticks(axHM, linewidth = 1)
clean_axis(axHM)
#create axes divider
divider = make_axes_locatable(axHM)
#draw cell/sample groups
axSG = divider.append_axes("bottom", size= bar_size, pad=bar_pad)
axSG.set_xlim(0, len(cell_groups))
for pos, gr in enumerate(cell_groups):
axSG.axvspan(pos, pos+1, color=cmap[gr])
remove_ticks(axSG, linewidth = 1)
clean_axis(axSG)
#draw Sca1 metadata
axSCA = divider.append_axes("top", size= bar_size, pad=bar_pad)
axSCA.set_xlim(0, len(cell_groups))
for pos, ix in enumerate(cell_groups.index):
axSCA.axvspan(pos, pos+1, color=cmap_Sca1[meta.ix['green_flag', ix]])
remove_ticks(axSCA, linewidth = 1)
clean_axis(axSCA)
#draw hair cycle metadata
axHC = divider.append_axes("top", size= bar_size, pad=bar_pad)
axHC.set_xlim(0, len(cell_groups))
for pos, ix in enumerate(cell_groups.index):
axHC.axvspan(pos, pos+1, color=cmap_hair_cycle[meta.ix['hair_cycle_stage', ix]])
remove_ticks(axHC, linewidth = 1)
clean_axis(axHC)
#draw cell size metadata
axSIZE = divider.append_axes("top", size= bar_size, pad=bar_pad)
axSIZE.set_xlim(0, len(cell_groups))
cmapSIZE = plt.cm.Greens
SIZE_max = meta.ix['diameter(um)', s_groups_1st.index].astype(float).max()
for pos, ix in enumerate(cell_groups.index):
val = float(meta.ix['diameter(um)', ix])
axSIZE.axvspan(pos, pos+1, color=cmapSIZE(val/SIZE_max))
remove_ticks(axSIZE, linewidth = 1)
clean_axis(axSIZE)
#draw transcript metadata
axTRANS = divider.append_axes("top", size= bar_size, pad=bar_pad)
axTRANS.set_xlim(0, len(cell_groups))
cmapTRANS = plt.cm.Reds
TRANS_max = meta.ix['sum_transcripts', s_groups_1st.index].astype(float).max()
for pos, ix in enumerate(cell_groups.index):
val = float(meta.ix['sum_transcripts', ix])
axTRANS.axvspan(pos, pos + 1, color = cmapTRANS(val / TRANS_max))
remove_ticks(axTRANS, linewidth = 1)
clean_axis(axTRANS)
figname = 'v1.8_S3_B_Heatmap_IFE_b.pdf'
plt.savefig('%s/%s' % (path_figures, figname),
format = 'pdf',
transparent = True,
bbox_inches = 'tight',
pad_inches = 0,
rasterized = True)
Barplots¶
In [206]:
genes_IFE_b = ['Krt14','Krt5','Mt2','Fth1','Thbs1','Avpi1','Krt16','Bhlhe40','Sostdc1', 'Aqp3','Postn','Krt79']
In [208]:
genes = genes_IFE_b
cell_groups = s_groups_IFE_b
dataset = seq[s_groups_IFE_b.index]
cmap = cmap_IFE_b
#create figure
bar_size = 0.5
bar_pad = 0.1
width = 10.0
height = len(genes) * 1.5 + (bar_size + bar_pad)
fig = plt.figure(facecolor = 'w', figsize = (width, height))
#create GS for gene barplots
gs1 = plt.GridSpec(len(genes), 1, hspace = 0.00, wspace = 0.0, top = 1, right = 1,
left = (bar_pad + bar_size) / width, bottom = (bar_pad + bar_size) / height)
#create GS for sample group bar
gs0 = plt.GridSpec(1,1, left = (bar_pad + bar_size) / width, right = 1, top = bar_size / height,
bottom = 0, hspace = 0.0, wspace = 0.0)
#colorlist bar
clistBAR = [cmap[val] for val in cell_groups]
#draw genes
for g in genes:
#create group_specific mean expressions
mean_tmp = pd.Series(index = set(cell_groups))
for gr in set(cell_groups):
ix_tmp = cell_groups[cell_groups == gr].index
mean_tmp[gr] = dataset.ix[g, ix_tmp].mean()
#create axis
ax0 = plt.subplot(gs1[genes.index(g)])
#define xaxis
ax0.set_xlim(left = 0, right = (len(dataset.columns)))
ax0.xaxis.set_ticklabels([])
#define yaxis
ax0.set_ylim(bottom = 0, top = np.ceil(np.max(mean_tmp) * 2))
ax0.set_ylabel(g, family = 'Liberation Sans', fontsize = 40, rotation = 'horizontal', va = 'center', ha = 'left')
ax0.yaxis.set_label_coords(1.02, 0.5)
for tick_pos in range(len(ax0.yaxis.get_major_ticks())):
if tick_pos == 0 or tick_pos == len(ax0.yaxis.get_major_ticks()) / 2:
ax0.yaxis.get_major_ticks()[tick_pos].label.set_family('Liberation Sans')
ax0.yaxis.get_major_ticks()[tick_pos].label.set_fontsize(35)
else:
ax0.yaxis.get_major_ticks()[tick_pos].label.set_visible(False)
#draw expression data
ax0.bar(np.arange(0, len(dataset.columns),1), dataset.ix[g], color = clistBAR, linewidth = 0, width = 1.0)
#draw means
ax0.step(range(len(cell_groups)), [mean_tmp[val] for val in cell_groups], where = 'post', color = 'black', linewidth = 2)
ax1 = plt.subplot(gs0[0])
ax1.set_xlim(0, len(cell_groups))
for s_gr in enumerate(cell_groups):
ax1.axvspan(xmin = s_gr[0], xmax = s_gr[0] + 1, color = cmap[s_gr[1]])
remove_ticks(ax1, linewidth = 1)
clean_axis(ax1)
figname = 'v1.8_2_A_Markers_IFE_b.pdf'
plt.savefig('%s/%s' % (path_figures, figname),
format = 'pdf',
transparent = True,
bbox_inches = 'tight',
pad_inches = 0,
rasterized = True)
Marker vs baseline¶
In [57]:
genes_baseline_IFE_b = neg_binom_extract_genes(NBR_2nd_size_bl[[0,1,2]], score = 'percentile - 25', number = 10)
In [268]:
#define input variables
gr_order = return_unique(s_groups_IFE_b)
genes = genes_baseline_IFE_b
L = len(genes.index)
traces = NBR_2nd_traces
cmap = cmap_IFE_b
to_2nd = IFE_b_to_2nd
#initialize figure
height = 5 * len(gr_order)
width = 6
plt.figure(facecolor = 'w', figsize = (width, height))
gs = plt.GridSpec(len(gr_order), 2, hspace = 0.15, wspace = 0.0, width_ratios = [5,8])
#iterate through groups
for pos, gr in enumerate(gr_order):
gr_2nd = to_2nd[gr]
genes_tmp = [gene for gene in genes[str(gr_2nd)].values if gene != None]
#create gene name axis
ax0 = plt.subplot(gs[pos,0])
ax0.set_ylim(L - 0.5, -0.5)
clean_axis(ax0)
#create violin plot axis
ax1 = plt.subplot(gs[pos,1])
ax1.spines['right'].set_color('none')
ax1.set_ylim(L - 0.5, -0.5)
ax1.yaxis.set_ticks_position('left')
ax1.yaxis.set_ticks([])
#iterate over genes
for ix, g in enumerate(genes_tmp):
ax0.text(0.975, ix, g, family = 'Liberation Sans', fontsize = 30, ha = 'right', va = 'center')
#print background
if ix % 2 == 0:
ax1.axhspan(ix - 0.5, ix + 0.5, color = '#F6F6F6', zorder = 0)
#print group traces
violin = ax1.violinplot([x - 1 for x in traces.ix[g, str(gr_2nd)]],
positions = [ix],
vert=False,
showextrema=False)
for v in violin['bodies']:
v.set_edgecolor(cmap[gr])
v.set_facecolor(cmap[gr])
v.set_linewidth(1.0)
v.set_alpha(1.0)
#print baseline traces
violin = ax1.violinplot([x - 1 for x in traces.ix[g, 'Baseline']],
positions = [ix],
vert=False,
showextrema=False)
for v in violin['bodies']:
v.set_edgecolor('#AAAAAA')
v.set_facecolor('#AAAAAA')
v.set_linewidth(1.0)
v.set_alpha(1.0)
for ix, t in enumerate(ax1.xaxis.get_ticklabels()):
if ix % 2 == 0:
t.set_family('Liberation Sans')
t.set_fontsize(25)
else:
t.set_fontsize(0)
figname = 'v1.8_S3_C_Markers_vs_Baseline_IFE_b.pdf'
plt.savefig('%s/%s' % (path_figures, figname),
format = 'pdf',
transparent = True,
bbox_inches = 'tight',
pad_inches = 0,
rasterized = True)
Marker vs. other groups¶
In [65]:
NBR_2nd_bin_gr_IFE_b, NBR_2nd_size_gr_IFE_b = neg_binom_vs_groups_v1P(NBR_2nd_summary[['0','1','2','Baseline']], dview, PP='95')
In [60]:
genes_groups_IFE_b = neg_binom_extract_genes(NBR_2nd_size_gr_IFE_b, score = 'percentile - 25', number = 10)
In [267]:
#define input variables
gr_order = return_unique(s_groups_IFE_b)
genes = genes_groups_IFE_b
L = len(genes.index)
traces = NBR_2nd_traces
summary = NBR_2nd_summary[['0','1','2','Baseline']]
cmap = cmap_IFE_b
to_2nd = IFE_b_to_2nd
#initialize figure
height = 5 * len(gr_order)
width = 6
plt.figure(facecolor = 'w', figsize = (width, height))
gs = plt.GridSpec(len(gr_order), 2, hspace = 0.15, wspace = 0.0, width_ratios = [5,8])
#iterate through groups
for pos, gr in enumerate(gr_order):
gr_2nd = to_2nd[gr]
genes_tmp = [gene for gene in genes[str(gr_2nd)].values if pd.isnull(gene) == False]
#create gene name axis
ax0 = plt.subplot(gs[pos,0])
ax0.set_ylim(L - 0.5, -0.5)
clean_axis(ax0)
#create violin plot axis
ax1 = plt.subplot(gs[pos,1])
ax1.spines['right'].set_color('none')
ax1.set_ylim(L - 0.5, -0.5)
ax1.yaxis.set_ticks_position('left')
ax1.yaxis.set_ticks([])
if len(genes_tmp) == 0:
remove_ticks(ax1)
#iterate over genes
for ix, g in enumerate(genes_tmp):
ax0.text(0.975, ix, g, family = 'Liberation Sans', fontsize = 30, ha = 'right', va = 'center')
#print background
if ix % 2 == 0:
ax1.axhspan(ix - 0.5, ix + 0.5, color = '#F6F6F6', zorder = 0)
#print group traces
violin = ax1.violinplot([x - 1 for x in traces.ix[g, str(gr_2nd)]],
positions = [ix],
vert=False,
showextrema=False)
for v in violin['bodies']:
v.set_edgecolor(cmap[gr])
v.set_facecolor(cmap[gr])
v.set_linewidth(1.0)
v.set_alpha(1.0)
#print 2nd group traces
gr_sec = summary.ix[g].ix['mean'].order().index[-2]
violin = ax1.violinplot([x - 1 for x in traces.ix[g, gr_sec]],
positions = [ix],
vert=False,
showextrema=False)
for v in violin['bodies']:
v.set_edgecolor('#AAAAAA')
v.set_facecolor('#AAAAAA')
v.set_linewidth(1.0)
v.set_alpha(1.0)
for ix, t in enumerate(ax1.xaxis.get_ticklabels()):
if ix % 2 == 0:
t.set_family('Liberation Sans')
t.set_fontsize(25)
else:
t.set_fontsize(0)
figname = 'v1.8_S3_C_Markers_vs_Groups_IFE_b.pdf'
plt.savefig('%s/%s' % (path_figures, figname),
format = 'pdf',
transparent = True,
bbox_inches = 'tight',
pad_inches = 0,
rasterized = True)
Robustness¶
In [290]:
robustness = robustness_IFE_b
robustness_null = robustness_IFE_b
cell_groups = s_groups_IFE_b
cmap = cmap_IFE_b
#generate figure
fig = plt.figure(facecolor = 'w', figsize = (14, 8))
gs = plt.GridSpec(nrows = 2, ncols = 1, wspace=0.0, hspace=0.0, height_ratios=[1.0, 11.0])
#generate plot
ax = plt.subplot(gs[1])
#generate xaxis
ax.set_xlim(- 0.5, len(set(cell_groups)) - 0.5)
ax.set_xticks(range(len(set(cell_groups))))
ax.set_xticklabels(np.arange(0, len(set(cell_groups))) + 1, family = 'Liberation Sans', fontsize = 35)
ax.set_xlabel('Affinity propagation cluster #', family = 'Liberation Sans', fontsize = 40)
ax.xaxis.set_label_coords(0.5, -0.1)
ax.tick_params(axis='x', which='major', pad=10)
#generate yaxis
ax.set_ylim(0, 1)
ax.set_yticks(np.arange(0, 1.1, 0.1))
ax.set_yticklabels([0.0,'',0.2,'',0.4,'',0.6,'',0.8,'',1.0], family = 'Liberation Sans', fontsize = 35)
ax.set_ylabel('Fraction of cell retained', family = 'Liberation Sans', fontsize = 40)
ax.yaxis.set_label_coords(-0.075, 0.5)
ax.tick_params(axis='y', which='major', pad=10)
#plot data
for ix, gr in enumerate(return_unique(cell_groups)):
ax.scatter([ix - (np.random.random() * 0.4 - 0.2) for pos in robustness[str(gr)]],
robustness[str(gr)],
s = 50,
color = 'blue')
ax.plot([ix - 0.25, ix + 0.25],
[np.mean(robustness[str(gr)])] * 2,
linewidth = 5,
color = 'black')
ax.scatter([ix - (np.random.random() * 0.4 - 0.2) for pos in robustness[str(gr)]],
robustness_null[str(gr)],
s = 50,
color = 'red')
ax.plot([ix - 0.25, ix + 0.25],
[np.mean(robustness_null[str(gr)])] * 2,
linewidth = 5,
color = 'black')
#plot color symbols
ax_Sym = plt.subplot(gs[0])
ax_Sym.set_xlim(- 0.5, len(set(cell_groups)) - 0.5)
ax_Sym.set_ylim(-0.5, 0.5)
for ix, gr in enumerate(return_unique(cell_groups)):
ax_Sym.scatter(ix,
0.0,
marker = 'o',
s = 750,
color = cmap[gr])
clean_axis(ax_Sym)
figname = 'v1.8_S3_g_Robustness_IFE_b.pdf'
plt.savefig('%s/%s' % (path_figures, figname),
format = 'pdf',
transparent = True,
bbox_inches = 'tight',
pad_inches = 0,
rasterized = True)
In [200]:
cell_groups = s_groups_IFE_b
group_pos = [(0.5,0),(0.5,1), (0.5,2)]
cmap = cmap_IFE_b
nmap = nmap_IFE_b
nmap_genes = gmap_IFE_b
#initialize figure
width = 16
height = 8
fig = plt.figure(facecolor = 'w', figsize = (width, height))
ax0 = plt.subplot()
ax0.set_ylim(2.5, -0.5)
ax0.set_xlim(0, 10)
group_list = return_unique(cell_groups)
for pos, gr in enumerate(group_list):
pos_tmp = group_pos[pos]
ax0.scatter(pos_tmp[0], pos_tmp[1], c = cmap[gr], marker = 'o', vmin = 0, vmax = 1.0, s = 750,
linewidth = 0.0, edgecolor = cmap[gr])
ax0.text(pos_tmp[0] + 0.5, pos_tmp[1], nmap[gr], family = 'Liberation Sans',
fontsize = 40, va = 'center', ha = 'left')
ax0.text(pos_tmp[0] + 0.5, pos_tmp[1] + 0.5, nmap_genes[group_list[pos]],
fontsize = 30, family = 'Liberation Sans', va = 'center', ha = 'left',
fontstyle = 'italic')
clean_axis(ax0)
figname = 'v1.8_2_A_Legend_IFE_b.pdf'
plt.savefig('%s/%s' % (path_figures, figname),
format = 'pdf',
transparent = True,
bbox_inches = 'tight',
pad_inches = 0,
rasterized = True)
Upper HF cells¶
t-SNE¶
In [307]:
tsne = tsne_1st
cell_groups = s_groups_uHF
cmap = cmap_uHF
#initialize figure
height = 15
width = 15
fig = plt.figure(facecolor = 'w', figsize = (width, height))
#define x- and y-limits
x_min, x_max = np.min(tsne['x']), np.max(tsne['x'])
y_min, y_max = np.min(tsne['y']), np.max(tsne['y'])
x_diff, y_diff = x_max - x_min, y_max - y_min
pad = 2.0
if x_diff > y_diff:
xlim = (x_min - 1, x_max + 3)
ylim = (y_min * (x_diff / y_diff) - pad, y_max * (x_diff / y_diff) + pad)
if x_diff < y_diff:
xlim = (x_min * (y_diff/x_diff) - 1, x_max * (y_diff/x_diff) + 3)
ylim = (y_min - pad, y_max + pad)
text_pad = 2
ix_sel = cell_groups.index
#draw groups in background
ax1 = plt.subplot()
ax1.set_xlim(xlim)
ax1.set_ylim(ylim)
remove_ticks(ax1)
ax1.scatter(tsne['x'],
tsne['y'],
s = 150,
linewidth = 0.0,
c = 'silver',
edgecolor = 'silver')
clist_tsne = [cmap[cell_groups[ix]] for ix in ix_sel]
ax1.scatter(tsne.ix[ix_sel, 'x'],
tsne.ix[ix_sel, 'y'],
s = 150,
linewidth = 0.0,
c = clist_tsne,
edgecolors = clist_tsne)
clean_axis(ax1)
figname = 'v1.8_2_B_t-SNE_uHF.pdf'
plt.savefig('%s/%s' % (path_figures, figname),
format = 'pdf',
transparent = True,
bbox_inches = 'tight',
pad_inches = 0,
rasterized = True)
In [308]:
#initialize figure
height = 5
width = 5
fig = plt.figure(facecolor = 'w', figsize = (width, height))
#draw inset
ax_inset = plt.subplot()
remove_ticks(ax_inset)
clist_tsne_inset = [cmap_1st[s_groups_1st[ix]] for ix in ix_sel]
ax_inset.scatter(tsne['x'],
tsne['y'],
s = 25,
linewidth = 0.0,
c = 'silver',
cmap = plt.cm.jet,
vmin = 0, vmax = 1.0)
ax_inset.scatter(tsne.ix[ix_sel, 'x'],
tsne.ix[ix_sel, 'y'],
s = 25,
linewidth = 0.0,
c = clist_tsne_inset,
edgecolor = clist_tsne_inset)
for s in ['top','bottom','left','right']:
ax_inset.spines[s].set_linewidth(1)
figname = 'v1.8_S3_A_t-SNE_uHF_Inset.pdf'
plt.savefig('%s/%s' % (path_figures, figname),
format = 'pdf',
transparent = True,
bbox_inches = 'tight',
pad_inches = 0,
rasterized = True)
Heatmap¶
In [111]:
#Transfer the expression data to relative values (0 [min] - 1 [max])
seq_sel_uHF = log2Transform(seq).ix[g_groups_uHF.index, s_groups_uHF.index]
seq_sel_uHF = seq_sel_uHF.apply(lambda x: x / max(x), axis = 1)
In [263]:
dataset = seq_sel_uHF
cell_groups = s_groups_uHF
gene_groups = g_groups_uHF
cmap = cmap_uHF
#create figure
bar_size = 0.75 #size of group and metadata bars
bar_pad = 0.1 #pad size of group and metadata bars
width = 0.04 * len(dataset.columns)
height = 32.0
#height = ((width - bar_size) / len(s_groups)) * len(g_groups) + 5 * bar_size
plt.figure(figsize=(width, height), facecolor = 'w')
#generate main axes
axHM = plt.axes()
axHM.set_position([0.00, 0.00, 1.0, 1.0])
#plot heatmap
axHM.matshow(dataset,
cmap = plt.cm.jet,
aspect = 'auto',
interpolation = 'nearest',
vmin = 0,
vmax = 1)
remove_ticks(axHM, linewidth = 1)
clean_axis(axHM)
#create axes divider
divider = make_axes_locatable(axHM)
#draw cell/sample groups
axSG = divider.append_axes("bottom", size= bar_size, pad=bar_pad)
axSG.set_xlim(0, len(cell_groups))
for pos, gr in enumerate(cell_groups):
axSG.axvspan(pos, pos+1, color=cmap[gr])
remove_ticks(axSG, linewidth = 1)
clean_axis(axSG)
#draw Sca1 metadata
axSCA = divider.append_axes("top", size= bar_size, pad=bar_pad)
axSCA.set_xlim(0, len(cell_groups))
for pos, ix in enumerate(cell_groups.index):
axSCA.axvspan(pos, pos+1, color=cmap_Sca1[meta.ix['green_flag', ix]])
remove_ticks(axSCA, linewidth = 1)
clean_axis(axSCA)
#draw hair cycle metadata
axHC = divider.append_axes("top", size= bar_size, pad=bar_pad)
axHC.set_xlim(0, len(cell_groups))
for pos, ix in enumerate(cell_groups.index):
axHC.axvspan(pos, pos+1, color=cmap_hair_cycle[meta.ix['hair_cycle_stage', ix]])
remove_ticks(axHC, linewidth = 1)
clean_axis(axHC)
#draw cell size metadata
axSIZE = divider.append_axes("top", size= bar_size, pad=bar_pad)
axSIZE.set_xlim(0, len(cell_groups))
cmapSIZE = plt.cm.Greens
SIZE_max = meta.ix['diameter(um)', s_groups_1st.index].astype(float).max()
for pos, ix in enumerate(cell_groups.index):
val = float(meta.ix['diameter(um)', ix])
axSIZE.axvspan(pos, pos+1, color=cmapSIZE(val/SIZE_max))
remove_ticks(axSIZE, linewidth = 1)
clean_axis(axSIZE)
#draw transcript metadata
axTRANS = divider.append_axes("top", size= bar_size, pad=bar_pad)
axTRANS.set_xlim(0, len(cell_groups))
cmapTRANS = plt.cm.Reds
TRANS_max = meta.ix['sum_transcripts', s_groups_1st.index].astype(float).max()
for pos, ix in enumerate(cell_groups.index):
val = float(meta.ix['sum_transcripts', ix])
axTRANS.axvspan(pos, pos + 1, color = cmapTRANS(val / TRANS_max))
remove_ticks(axTRANS, linewidth = 1)
clean_axis(axTRANS)
figname = 'v1.8_S3_B_Heatmap_uHF.pdf'
plt.savefig('%s/%s' % (path_figures, figname),
format = 'pdf',
transparent = True,
bbox_inches = 'tight',
pad_inches = 0,
rasterized = True)
Barplots¶
In [96]:
genes_uHF = ['Krt79','Krt17','Klk10','Cryab','Rbp1','Defb6','Cst6','Krt14','Lrig1','Krt5','Ptn','Krt10','Ptgs1','Lor','Flg2']
In [99]:
genes = genes_uHF
cell_groups = s_groups_uHF
dataset = seq[s_groups_uHF.index]
cmap = cmap_uHF
#create figure
bar_size = 0.5
bar_pad = 0.1
width = 10.0
height = len(genes) * 1.20 + (bar_size + bar_pad)
fig = plt.figure(facecolor = 'w', figsize = (width, height))
#create GS for gene barplots
gs1 = plt.GridSpec(len(genes), 1, hspace = 0.00, wspace = 0.0, top = 1, right = 1,
left = (bar_pad + bar_size) / width, bottom = (bar_pad + bar_size) / height)
#create GS for sample group bar
gs0 = plt.GridSpec(1,1, left = (bar_pad + bar_size) / width, right = 1, top = bar_size / height,
bottom = 0, hspace = 0.0, wspace = 0.0)
#colorlist bar
clistBAR = [cmap[val] for val in cell_groups]
#draw genes
for g in genes:
#create group_specific mean expressions
mean_tmp = pd.Series(index = set(cell_groups))
for gr in set(cell_groups):
ix_tmp = cell_groups[cell_groups == gr].index
mean_tmp[gr] = dataset.ix[g, ix_tmp].mean()
#create axis
ax0 = plt.subplot(gs1[genes.index(g)])
#define xaxis
ax0.set_xlim(left = 0, right = (len(dataset.columns)))
ax0.xaxis.set_ticklabels([])
#define yaxis
ax0.set_ylim(bottom = 0, top = np.ceil(np.max(mean_tmp) * 2))
ax0.set_ylabel(g, family = 'Liberation Sans', fontsize = 40, rotation = 'horizontal', va = 'center', ha = 'left')
ax0.yaxis.set_label_coords(1.02, 0.5)
for tick_pos in range(len(ax0.yaxis.get_major_ticks())):
if tick_pos == 0 or tick_pos == len(ax0.yaxis.get_major_ticks()) / 2:
ax0.yaxis.get_major_ticks()[tick_pos].label.set_family('Liberation Sans')
ax0.yaxis.get_major_ticks()[tick_pos].label.set_fontsize(35)
else:
ax0.yaxis.get_major_ticks()[tick_pos].label.set_visible(False)
#draw expression data
ax0.bar(np.arange(0, len(dataset.columns),1), dataset.ix[g], color = clistBAR, linewidth = 0, width = 1.0)
#draw means
ax0.step(range(len(cell_groups)), [mean_tmp[val] for val in cell_groups], where = 'post', color = 'black', linewidth = 2)
ax1 = plt.subplot(gs0[0])
ax1.set_xlim(0, len(cell_groups))
for s_gr in enumerate(cell_groups):
ax1.axvspan(xmin = s_gr[0], xmax = s_gr[0] + 1, color = cmap[s_gr[1]])
remove_ticks(ax1, linewidth = 1)
clean_axis(ax1)
figname = 'v1.8_2_B_Markers_uHF.pdf'
plt.savefig('%s/%s' % (path_figures, figname),
format = 'pdf',
transparent = True,
bbox_inches = 'tight',
pad_inches = 0,
rasterized = True)
Markers against baseline¶
In [117]:
genes_baseline_uHF = neg_binom_extract_genes(NBR_2nd_size_bl, score = 'percentile - 25', number = 10)
In [269]:
#define input variables
gr_order = return_unique(s_groups_uHF)
genes = genes_baseline_uHF
L = len(genes.index)
traces = NBR_2nd_traces
cmap = cmap_uHF
to_2nd = uHF_to_2nd
#initialize figure
height = 5 * len(gr_order)
width = 6
plt.figure(facecolor = 'w', figsize = (width, height))
gs = plt.GridSpec(len(gr_order), 2, hspace = 0.15, wspace = 0.0, width_ratios = [5,8])
#iterate through groups
for pos, gr in enumerate(gr_order):
gr_2nd = to_2nd[gr]
genes_tmp = [gene for gene in genes[str(gr_2nd)].values if gene != None]
#create gene name axis
ax0 = plt.subplot(gs[pos,0])
ax0.set_ylim(L - 0.5, -0.5)
clean_axis(ax0)
#create violin plot axis
ax1 = plt.subplot(gs[pos,1])
ax1.spines['right'].set_color('none')
ax1.set_ylim(L - 0.5, -0.5)
ax1.yaxis.set_ticks_position('left')
ax1.yaxis.set_ticks([])
#iterate over genes
for ix, g in enumerate(genes_tmp):
ax0.text(0.975, ix, g, family = 'Liberation Sans', fontsize = 30, ha = 'right', va = 'center')
#print background
if ix % 2 == 0:
ax1.axhspan(ix - 0.5, ix + 0.5, color = '#F6F6F6', zorder = 0)
#print group traces
violin = ax1.violinplot([x - 1 for x in traces.ix[g, str(gr_2nd)]],
positions = [ix],
vert=False,
showextrema=False)
for v in violin['bodies']:
v.set_edgecolor(cmap[gr])
v.set_facecolor(cmap[gr])
v.set_linewidth(1.0)
v.set_alpha(1.0)
#print baseline traces
violin = ax1.violinplot([x - 1 for x in traces.ix[g, 'Baseline']],
positions = [ix],
vert=False,
showextrema=False)
for v in violin['bodies']:
v.set_edgecolor('#AAAAAA')
v.set_facecolor('#AAAAAA')
v.set_linewidth(1.0)
v.set_alpha(1.0)
for ix, t in enumerate(ax1.xaxis.get_ticklabels()):
if ix % 2 == 0:
t.set_family('Liberation Sans')
t.set_fontsize(25)
else:
t.set_fontsize(0)
figname = 'v1.8_S3_D_Markers_vs_Baseline_uHF.pdf'
plt.savefig('%s/%s' % (path_figures, figname),
format = 'pdf',
transparent = True,
bbox_inches = 'tight',
pad_inches = 0,
rasterized = True)
Markers vs. other groups¶
In [66]:
NBR_2nd_bin_gr_uHF, NBR_2nd_size_gr_uHF = neg_binom_vs_groups_v1P(NBR_2nd_summary[['7','8','9','10','11','12','13','Baseline']], dview, PP='95')
In [121]:
genes_groups_uHF = neg_binom_extract_genes(NBR_2nd_size_gr_uHF, score = 'percentile - 25', number = 10)
In [270]:
#define input variables
gr_order = return_unique(s_groups_uHF)
genes = genes_groups_uHF
L = len(genes.index)
traces = NBR_2nd_traces
summary = NBR_2nd_summary[['7','8','9','10','11','12','13','Baseline']]
cmap = cmap_uHF
to_2nd = uHF_to_2nd
#initialize figure
height = 5 * len(gr_order)
width = 6
plt.figure(facecolor = 'w', figsize = (width, height))
gs = plt.GridSpec(len(gr_order), 2, hspace = 0.15, wspace = 0.0, width_ratios = [5,8])
#iterate through groups
for pos, gr in enumerate(gr_order):
gr_2nd = to_2nd[gr]
genes_tmp = [gene for gene in genes[str(gr_2nd)].values if pd.isnull(gene) == False]
#create gene name axis
ax0 = plt.subplot(gs[pos,0])
ax0.set_ylim(L - 0.5, -0.5)
clean_axis(ax0)
#create violin plot axis
ax1 = plt.subplot(gs[pos,1])
ax1.spines['right'].set_color('none')
ax1.set_ylim(L - 0.5, -0.5)
ax1.yaxis.set_ticks_position('left')
ax1.yaxis.set_ticks([])
if len(genes_tmp) == 0:
remove_ticks(ax1)
#iterate over genes
for ix, g in enumerate(genes_tmp):
ax0.text(0.975, ix, g, family = 'Liberation Sans', fontsize = 30, ha = 'right', va = 'center')
#print background
if ix % 2 == 0:
ax1.axhspan(ix - 0.5, ix + 0.5, color = '#F6F6F6', zorder = 0)
#print group traces
violin = ax1.violinplot([x - 1 for x in traces.ix[g, str(gr_2nd)]],
positions = [ix],
vert=False,
showextrema=False)
for v in violin['bodies']:
v.set_edgecolor(cmap[gr])
v.set_facecolor(cmap[gr])
v.set_linewidth(1.0)
v.set_alpha(1.0)
#print 2nd group traces
gr_sec = summary.ix[g].ix['mean'].order().index[-2]
violin = ax1.violinplot([x - 1 for x in traces.ix[g, gr_sec]],
positions = [ix],
vert=False,
showextrema=False)
for v in violin['bodies']:
v.set_edgecolor('#AAAAAA')
v.set_facecolor('#AAAAAA')
v.set_linewidth(1.0)
v.set_alpha(1.0)
for ix, t in enumerate(ax1.xaxis.get_ticklabels()):
if ix % 2 == 0:
t.set_family('Liberation Sans')
t.set_fontsize(25)
else:
t.set_fontsize(0)
figname = 'v1.8_S3_D_Markers_vs_Groups_uHF.pdf'
plt.savefig('%s/%s' % (path_figures, figname),
format = 'pdf',
transparent = True,
bbox_inches = 'tight',
pad_inches = 0,
rasterized = True)
Robustness¶
In [289]:
robustness = robustness_uHF
robustness_null = robustness_uHF_null
cell_groups = s_groups_uHF
cmap = cmap_uHF
#generate figure
fig = plt.figure(facecolor = 'w', figsize = (14, 8))
gs = plt.GridSpec(nrows = 2, ncols = 1, wspace=0.0, hspace=0.0, height_ratios=[1.0, 11.0])
#generate plot
ax = plt.subplot(gs[1])
#generate xaxis
ax.set_xlim(- 0.5, len(set(cell_groups)) - 0.5)
ax.set_xticks(range(len(set(cell_groups))))
ax.set_xticklabels(np.arange(0, len(set(cell_groups))) + 1, family = 'Liberation Sans', fontsize = 35)
ax.set_xlabel('Affinity propagation cluster #', family = 'Liberation Sans', fontsize = 40)
ax.xaxis.set_label_coords(0.5, -0.1)
ax.tick_params(axis='x', which='major', pad=10)
#generate yaxis
ax.set_ylim(0, 1)
ax.set_yticks(np.arange(0, 1.1, 0.1))
ax.set_yticklabels([0.0,'',0.2,'',0.4,'',0.6,'',0.8,'',1.0], family = 'Liberation Sans', fontsize = 35)
ax.set_ylabel('Fraction of cell retained', family = 'Liberation Sans', fontsize = 40)
ax.yaxis.set_label_coords(-0.075, 0.5)
ax.tick_params(axis='y', which='major', pad=10)
#plot data
for ix, gr in enumerate(return_unique(cell_groups)):
ax.scatter([ix - (np.random.random() * 0.4 - 0.2) for pos in robustness[str(gr)]],
robustness[str(gr)],
s = 50,
color = 'blue')
ax.plot([ix - 0.25, ix + 0.25],
[np.mean(robustness[str(gr)])] * 2,
linewidth = 5,
color = 'black')
ax.scatter([ix - (np.random.random() * 0.4 - 0.2) for pos in robustness[str(gr)]],
robustness_null[str(gr)],
s = 50,
color = 'red')
ax.plot([ix - 0.25, ix + 0.25],
[np.mean(robustness_null[str(gr)])] * 2,
linewidth = 5,
color = 'black')
#plot color symbols
ax_Sym = plt.subplot(gs[0])
ax_Sym.set_xlim(- 0.5, len(set(cell_groups)) - 0.5)
ax_Sym.set_ylim(-0.5, 0.5)
for ix, gr in enumerate(return_unique(cell_groups)):
ax_Sym.scatter(ix,
0.0,
marker = '^',
s = 750,
color = cmap[gr])
clean_axis(ax_Sym)
figname = 'v1.8_S3_H_Robustness_uHF.pdf'
plt.savefig('%s/%s' % (path_figures, figname),
format = 'pdf',
transparent = True,
bbox_inches = 'tight',
pad_inches = 0,
rasterized = True)
In [233]:
cell_groups = s_groups_uHF
group_pos = [(0.5,0),(0.5,1),(0.5,2),(0.5,3),(5.5,0),(5.5,1),(5.5,2)]
cmap = cmap_uHF
nmap = nmap_uHF
nmap_genes = gmap_uHF
#initialize figure
width = 16
height = 14
fig = plt.figure(facecolor = 'w', figsize = (width, height))
ax0 = plt.subplot()
ax0.set_ylim(3.75, -0.25)
ax0.set_xlim(0, 10)
group_list = return_unique(cell_groups)
for pos, gr in enumerate(group_list):
pos_tmp = group_pos[pos]
ax0.scatter(pos_tmp[0], pos_tmp[1], c = cmap[gr], marker = '^', vmin = 0, vmax = 1.0, s = 750,
linewidth = 0.0, edgecolor = cmap[gr])
ax0.text(pos_tmp[0] + 0.5, pos_tmp[1], nmap[gr], family = 'Liberation Sans',
fontsize = 40, va = 'center', ha = 'left')
ax0.text(pos_tmp[0] + 0.5, pos_tmp[1] + 0.5, nmap_genes[group_list[pos]],
fontsize = 30, family = 'Liberation Sans', va = 'center', ha = 'left',
fontstyle = 'italic')
#clean_axis(ax0)
figname = 'v1.8_2_B_Legend_uHF.pdf'
plt.savefig('%s/%s' % (path_figures, figname),
format = 'pdf',
transparent = True,
bbox_inches = 'tight',
pad_inches = 0,
rasterized = True)
Outer bulge cells¶
t-SNE¶
In [309]:
tsne = tsne_1st
cell_groups = s_groups_OB
cmap = cmap_OB
#initialize figure
height = 15
width = 15
fig = plt.figure(facecolor = 'w', figsize = (width, height))
#define x- and y-limits
x_min, x_max = np.min(tsne['x']), np.max(tsne['x'])
y_min, y_max = np.min(tsne['y']), np.max(tsne['y'])
x_diff, y_diff = x_max - x_min, y_max - y_min
pad = 2.0
if x_diff > y_diff:
xlim = (x_min - 1, x_max + 3)
ylim = (y_min * (x_diff / y_diff) - pad, y_max * (x_diff / y_diff) + pad)
if x_diff < y_diff:
xlim = (x_min * (y_diff/x_diff) - 1, x_max * (y_diff/x_diff) + 3)
ylim = (y_min - pad, y_max + pad)
text_pad = 2
ix_sel = cell_groups.index
#draw groups in background
ax1 = plt.subplot()
ax1.set_xlim(xlim)
ax1.set_ylim(ylim)
remove_ticks(ax1)
ax1.scatter(tsne['x'],
tsne['y'],
s = 150,
linewidth = 0.0,
c = 'silver',
edgecolor = 'silver')
clist_tsne = [cmap[cell_groups[ix]] for ix in ix_sel]
ax1.scatter(tsne.ix[ix_sel, 'x'],
tsne.ix[ix_sel, 'y'],
s = 150,
linewidth = 0.0,
c = clist_tsne,
edgecolors = clist_tsne)
clean_axis(ax1)
figname = 'v1.8_2_C_t-SNE_OB.pdf'
plt.savefig('%s/%s' % (path_figures, figname),
format = 'pdf',
transparent = True,
bbox_inches = 'tight',
pad_inches = 0,
rasterized = True)
In [310]:
#initialize figure
height = 5
width = 5
fig = plt.figure(facecolor = 'w', figsize = (width, height))
#draw inset
ax_inset = plt.subplot()
remove_ticks(ax_inset)
clist_tsne_inset = [cmap_1st[s_groups_1st[ix]] for ix in ix_sel]
ax_inset.scatter(tsne['x'],
tsne['y'],
s = 25,
linewidth = 0.0,
c = 'silver',
cmap = plt.cm.jet,
vmin = 0, vmax = 1.0)
ax_inset.scatter(tsne.ix[ix_sel, 'x'],
tsne.ix[ix_sel, 'y'],
s = 25,
linewidth = 0.0,
c = clist_tsne_inset,
edgecolor = clist_tsne_inset)
for s in ['top','bottom','left','right']:
ax_inset.spines[s].set_linewidth(1)
figname = 'v1.8_S3_A_t-SNE_OB_Inset.pdf'
plt.savefig('%s/%s' % (path_figures, figname),
format = 'pdf',
transparent = True,
bbox_inches = 'tight',
pad_inches = 0,
rasterized = True)
Heatmap¶
In [135]:
#Transfer the expression data to relative values (0 [min] - 1 [max])
seq_sel_OB = log2Transform(seq).ix[g_groups_OB.index, s_groups_OB.index]
seq_sel_OB = seq_sel_OB.apply(lambda x: x / max(x), axis = 1)
In [265]:
dataset = seq_sel_OB
cell_groups = s_groups_OB
gene_groups = g_groups_OB
cmap = cmap_OB
#create figure
bar_size = 0.75 #size of group and metadata bars
bar_pad = 0.1 #pad size of group and metadata bars
width = 0.04 * len(dataset.columns)
height = 32.0
#height = ((width - bar_size) / len(s_groups)) * len(g_groups) + 5 * bar_size
plt.figure(figsize=(width, height), facecolor = 'w')
#generate main axes
axHM = plt.axes()
axHM.set_position([0.00, 0.00, 1.0, 1.0])
#plot heatmap
axHM.matshow(dataset,
cmap = plt.cm.jet,
aspect = 'auto',
interpolation = 'nearest',
vmin = 0,
vmax = 1)
remove_ticks(axHM, linewidth = 1)
clean_axis(axHM)
#create axes divider
divider = make_axes_locatable(axHM)
#draw cell/sample groups
axSG = divider.append_axes("bottom", size= bar_size, pad=bar_pad)
axSG.set_xlim(0, len(cell_groups))
for pos, gr in enumerate(cell_groups):
axSG.axvspan(pos, pos+1, color=cmap[gr])
remove_ticks(axSG, linewidth = 1)
clean_axis(axSG)
#draw Sca1 metadata
axSCA = divider.append_axes("top", size= bar_size, pad=bar_pad)
axSCA.set_xlim(0, len(cell_groups))
for pos, ix in enumerate(cell_groups.index):
axSCA.axvspan(pos, pos+1, color=cmap_Sca1[meta.ix['green_flag', ix]])
remove_ticks(axSCA, linewidth = 1)
clean_axis(axSCA)
#draw hair cycle metadata
axHC = divider.append_axes("top", size= bar_size, pad=bar_pad)
axHC.set_xlim(0, len(cell_groups))
for pos, ix in enumerate(cell_groups.index):
axHC.axvspan(pos, pos+1, color=cmap_hair_cycle[meta.ix['hair_cycle_stage', ix]])
remove_ticks(axHC, linewidth = 1)
clean_axis(axHC)
#draw cell size metadata
axSIZE = divider.append_axes("top", size= bar_size, pad=bar_pad)
axSIZE.set_xlim(0, len(cell_groups))
cmapSIZE = plt.cm.Greens
SIZE_max = meta.ix['diameter(um)', s_groups_1st.index].astype(float).max()
for pos, ix in enumerate(cell_groups.index):
val = float(meta.ix['diameter(um)', ix])
axSIZE.axvspan(pos, pos+1, color=cmapSIZE(val/SIZE_max))
remove_ticks(axSIZE, linewidth = 1)
clean_axis(axSIZE)
#draw transcript metadata
axTRANS = divider.append_axes("top", size= bar_size, pad=bar_pad)
axTRANS.set_xlim(0, len(cell_groups))
cmapTRANS = plt.cm.Reds
TRANS_max = meta.ix['sum_transcripts', s_groups_1st.index].astype(float).max()
for pos, ix in enumerate(cell_groups.index):
val = float(meta.ix['sum_transcripts', ix])
axTRANS.axvspan(pos, pos + 1, color = cmapTRANS(val / TRANS_max))
remove_ticks(axTRANS, linewidth = 1)
clean_axis(axTRANS)
figname = 'v1.8_S3_B_Heatmap_OB.pdf'
plt.savefig('%s/%s' % (path_figures, figname),
format = 'pdf',
transparent = True,
bbox_inches = 'tight',
pad_inches = 0,
rasterized = True)
Barplots¶
In [92]:
genes_OB = ['Postn','Cd34','Lgr5','Krt24','Aspn','Nrep','Gli1','Lgr6','Krt15','Krt17','Krt79','Krt14','Krt5','Krt10','Ptgs1']
In [95]:
genes = genes_OB
cell_groups = s_groups_OB
dataset = seq[s_groups_OB.index]
cmap = cmap_OB
#create figure
bar_size = 0.5
bar_pad = 0.1
width = 10.0
height = len(genes) * 1.20 + (bar_size + bar_pad)
fig = plt.figure(facecolor = 'w', figsize = (width, height))
#create GS for gene barplots
gs1 = plt.GridSpec(len(genes), 1, hspace = 0.00, wspace = 0.0, top = 1, right = 1,
left = (bar_pad + bar_size) / width, bottom = (bar_pad + bar_size) / height)
#create GS for sample group bar
gs0 = plt.GridSpec(1,1, left = (bar_pad + bar_size) / width, right = 1, top = bar_size / height,
bottom = 0, hspace = 0.0, wspace = 0.0)
#colorlist bar
clistBAR = [cmap[val] for val in cell_groups]
#draw genes
for g in genes:
#create group_specific mean expressions
mean_tmp = pd.Series(index = set(cell_groups))
for gr in set(cell_groups):
ix_tmp = cell_groups[cell_groups == gr].index
mean_tmp[gr] = dataset.ix[g, ix_tmp].mean()
#create axis
ax0 = plt.subplot(gs1[genes.index(g)])
#define xaxis
ax0.set_xlim(left = 0, right = (len(dataset.columns)))
ax0.xaxis.set_ticklabels([])
#define yaxis
ax0.set_ylim(bottom = 0, top = np.ceil(np.max(mean_tmp) * 2))
ax0.set_ylabel(g, family = 'Liberation Sans', fontsize = 40, rotation = 'horizontal', va = 'center', ha = 'left')
ax0.yaxis.set_label_coords(1.02, 0.5)
for tick_pos in range(len(ax0.yaxis.get_major_ticks())):
if tick_pos == 0 or tick_pos == len(ax0.yaxis.get_major_ticks()) / 2:
ax0.yaxis.get_major_ticks()[tick_pos].label.set_family('Liberation Sans')
ax0.yaxis.get_major_ticks()[tick_pos].label.set_fontsize(35)
else:
ax0.yaxis.get_major_ticks()[tick_pos].label.set_visible(False)
#draw expression data
ax0.bar(np.arange(0, len(dataset.columns),1), dataset.ix[g], color = clistBAR, linewidth = 0, width = 1.0)
#draw means
ax0.step(range(len(cell_groups)), [mean_tmp[val] for val in cell_groups], where = 'post', color = 'black', linewidth = 2)
ax1 = plt.subplot(gs0[0])
ax1.set_xlim(0, len(cell_groups))
for s_gr in enumerate(cell_groups):
ax1.axvspan(xmin = s_gr[0], xmax = s_gr[0] + 1, color = cmap[s_gr[1]])
remove_ticks(ax1, linewidth = 1)
clean_axis(ax1)
figname = 'v1.8_2_C_Markers_OB.pdf'
plt.savefig('%s/%s' % (path_figures, figname),
format = 'pdf',
transparent = True,
bbox_inches = 'tight',
pad_inches = 0,
rasterized = True)
Markers vs. Baseline¶
In [141]:
genes_baseline_OB = neg_binom_extract_genes(NBR_2nd_size_bl, score = 'percentile - 25', number = 10)
In [271]:
#define input variables
gr_order = return_unique(s_groups_OB)
genes = genes_baseline_OB
L = len(genes.index)
traces = NBR_2nd_traces
cmap = cmap_OB
to_2nd = OB_to_2nd
#initialize figure
height = 5 * len(gr_order)
width = 6
plt.figure(facecolor = 'w', figsize = (width, height))
gs = plt.GridSpec(len(gr_order), 2, hspace = 0.15, wspace = 0.0, width_ratios = [5,8])
#iterate through groups
for pos, gr in enumerate(gr_order):
gr_2nd = to_2nd[gr]
genes_tmp = [gene for gene in genes[str(gr_2nd)].values if gene != None]
#create gene name axis
ax0 = plt.subplot(gs[pos,0])
ax0.set_ylim(L - 0.5, -0.5)
clean_axis(ax0)
#create violin plot axis
ax1 = plt.subplot(gs[pos,1])
ax1.spines['right'].set_color('none')
ax1.set_ylim(L - 0.5, -0.5)
ax1.yaxis.set_ticks_position('left')
ax1.yaxis.set_ticks([])
#iterate over genes
for ix, g in enumerate(genes_tmp):
ax0.text(0.975, ix, g, family = 'Liberation Sans', fontsize = 30, ha = 'right', va = 'center')
#print background
if ix % 2 == 0:
ax1.axhspan(ix - 0.5, ix + 0.5, color = '#F6F6F6', zorder = 0)
#print group traces
violin = ax1.violinplot([x - 1 for x in traces.ix[g, str(gr_2nd)]],
positions = [ix],
vert=False,
showextrema=False)
for v in violin['bodies']:
v.set_edgecolor(cmap[gr])
v.set_facecolor(cmap[gr])
v.set_linewidth(1.0)
v.set_alpha(1.0)
#print baseline traces
violin = ax1.violinplot([x - 1 for x in traces.ix[g, 'Baseline']],
positions = [ix],
vert=False,
showextrema=False)
for v in violin['bodies']:
v.set_edgecolor('#AAAAAA')
v.set_facecolor('#AAAAAA')
v.set_linewidth(1.0)
v.set_alpha(1.0)
for ix, t in enumerate(ax1.xaxis.get_ticklabels()):
if ix % 2 == 0:
t.set_family('Liberation Sans')
t.set_fontsize(25)
else:
t.set_fontsize(0)
figname = 'v1.8_S3_E_Markers_vs_Baseline_OB.pdf'
plt.savefig('%s/%s' % (path_figures, figname),
format = 'pdf',
transparent = True,
bbox_inches = 'tight',
pad_inches = 0,
rasterized = True)
Markers vs. other groups¶
In [67]:
NBR_2nd_bin_gr_OB, NBR_2nd_size_gr_OB = neg_binom_vs_groups_v1P(NBR_2nd_summary[['15','16','17','18','19','Baseline']], dview, PP='95')
In [146]:
genes_groups_OB = neg_binom_extract_genes(NBR_2nd_size_gr_OB, score = 'percentile - 25', number = 10)
In [272]:
#define input variables
gr_order = return_unique(s_groups_OB)
genes = genes_groups_OB
L = len(genes.index)
traces = NBR_2nd_traces
summary = NBR_2nd_summary[['15','16','17','18','19','Baseline']]
cmap = cmap_OB
to_2nd = OB_to_2nd
#initialize figure
height = 5 * len(gr_order)
width = 6
plt.figure(facecolor = 'w', figsize = (width, height))
gs = plt.GridSpec(len(gr_order), 2, hspace = 0.15, wspace = 0.0, width_ratios = [5,8])
#iterate through groups
for pos, gr in enumerate(gr_order):
gr_2nd = to_2nd[gr]
genes_tmp = [gene for gene in genes[str(gr_2nd)].values if pd.isnull(gene) == False]
#create gene name axis
ax0 = plt.subplot(gs[pos,0])
ax0.set_ylim(L - 0.5, -0.5)
clean_axis(ax0)
#create violin plot axis
ax1 = plt.subplot(gs[pos,1])
ax1.spines['right'].set_color('none')
ax1.set_ylim(L - 0.5, -0.5)
ax1.yaxis.set_ticks_position('left')
ax1.yaxis.set_ticks([])
if len(genes_tmp) == 0:
remove_ticks(ax1)
#iterate over genes
for ix, g in enumerate(genes_tmp):
ax0.text(0.975, ix, g, family = 'Liberation Sans', fontsize = 30, ha = 'right', va = 'center')
#print background
if ix % 2 == 0:
ax1.axhspan(ix - 0.5, ix + 0.5, color = '#F6F6F6', zorder = 0)
#print group traces
violin = ax1.violinplot([x - 1 for x in traces.ix[g, str(gr_2nd)]],
positions = [ix],
vert=False,
showextrema=False)
for v in violin['bodies']:
v.set_edgecolor(cmap[gr])
v.set_facecolor(cmap[gr])
v.set_linewidth(1.0)
v.set_alpha(1.0)
#print 2nd group traces
gr_sec = summary.ix[g].ix['mean'].order().index[-2]
violin = ax1.violinplot([x - 1 for x in traces.ix[g, gr_sec]],
positions = [ix],
vert=False,
showextrema=False)
for v in violin['bodies']:
v.set_edgecolor('#AAAAAA')
v.set_facecolor('#AAAAAA')
v.set_linewidth(1.0)
v.set_alpha(1.0)
for ix, t in enumerate(ax1.xaxis.get_ticklabels()):
if ix % 2 == 0:
t.set_family('Liberation Sans')
t.set_fontsize(25)
else:
t.set_fontsize(0)
figname = 'v1.8_S3_E_Markers_vs_Groups_OB.pdf'
plt.savefig('%s/%s' % (path_figures, figname),
format = 'pdf',
transparent = True,
bbox_inches = 'tight',
pad_inches = 0,
rasterized = True)
Robustness¶
In [288]:
robustness = robustness_OB
robustness_null = robustness_OB_null
cell_groups = s_groups_OB
cmap = cmap_OB
#generate figure
fig = plt.figure(facecolor = 'w', figsize = (14, 8))
gs = plt.GridSpec(nrows = 2, ncols = 1, wspace=0.0, hspace=0.0, height_ratios=[1.0, 11.0])
#generate plot
ax = plt.subplot(gs[1])
#generate xaxis
ax.set_xlim(- 0.5, len(set(cell_groups)) - 0.5)
ax.set_xticks(range(len(set(cell_groups))))
ax.set_xticklabels(np.arange(0, len(set(cell_groups))) + 1, family = 'Liberation Sans', fontsize = 35)
ax.set_xlabel('Affinity propagation cluster #', family = 'Liberation Sans', fontsize = 40)
ax.xaxis.set_label_coords(0.5, -0.1)
ax.tick_params(axis='x', which='major', pad=10)
#generate yaxis
ax.set_ylim(0, 1)
ax.set_yticks(np.arange(0, 1.1, 0.1))
ax.set_yticklabels([0.0,'',0.2,'',0.4,'',0.6,'',0.8,'',1.0], family = 'Liberation Sans', fontsize = 35)
ax.set_ylabel('Fraction of cell retained', family = 'Liberation Sans', fontsize = 40)
ax.yaxis.set_label_coords(-0.075, 0.5)
ax.tick_params(axis='y', which='major', pad=10)
#plot data
for ix, gr in enumerate(return_unique(cell_groups)):
ax.scatter([ix - (np.random.random() * 0.4 - 0.2) for pos in robustness[str(gr)]],
robustness[str(gr)],
s = 50,
color = 'blue')
ax.plot([ix - 0.25, ix + 0.25],
[np.mean(robustness[str(gr)])] * 2,
linewidth = 5,
color = 'black')
ax.scatter([ix - (np.random.random() * 0.4 - 0.2) for pos in robustness[str(gr)]],
robustness_null[str(gr)],
s = 50,
color = 'red')
ax.plot([ix - 0.25, ix + 0.25],
[np.mean(robustness_null[str(gr)])] * 2,
linewidth = 5,
color = 'black')
#plot color symbols
ax_Sym = plt.subplot(gs[0])
ax_Sym.set_xlim(- 0.5, len(set(cell_groups)) - 0.5)
ax_Sym.set_ylim(-0.5, 0.5)
for ix, gr in enumerate(return_unique(cell_groups)):
ax_Sym.scatter(ix,
0.0,
marker = 'D',
s = 500,
color = cmap[gr])
clean_axis(ax_Sym)
figname = 'v1.8_S3_I_Robustness_OB.pdf'
plt.savefig('%s/%s' % (path_figures, figname),
format = 'pdf',
transparent = True,
bbox_inches = 'tight',
pad_inches = 0,
rasterized = True)
In [231]:
cell_groups = s_groups_OB
group_pos = [(0.5,0),(0.5,1),(0.5,2),(5.5,0),(5.5,1),(5.5,2)]
cmap = cmap_OB
nmap = nmap_OB
nmap_genes = gmap_OB
#initialize figure
width = 16
height = 8
fig = plt.figure(facecolor = 'w', figsize = (width, height))
ax0 = plt.subplot()
ax0.set_ylim(2.75, -0.25)
ax0.set_xlim(0, 10)
group_list = return_unique(cell_groups)
for pos, gr in enumerate(group_list):
pos_tmp = group_pos[pos]
ax0.scatter(pos_tmp[0], pos_tmp[1], c = cmap[gr], marker = 'D', vmin = 0, vmax = 1.0, s = 500,
linewidth = 0.0, edgecolor = cmap[gr])
ax0.text(pos_tmp[0] + 0.5, pos_tmp[1], nmap[gr], family = 'Liberation Sans',
fontsize = 40, va = 'center', ha = 'left')
ax0.text(pos_tmp[0] + 0.5, pos_tmp[1] + 0.5, nmap_genes[group_list[pos]],
fontsize = 30, family = 'Liberation Sans', va = 'center', ha = 'left',
fontstyle = 'italic')
#clean_axis(ax0)
figname = 'v1.8_2_C_Legend_OB.pdf'
plt.savefig('%s/%s' % (path_figures, figname),
format = 'pdf',
transparent = True,
bbox_inches = 'tight',
pad_inches = 0,
rasterized = True)
Inner bulge¶
In [311]:
tsne = tsne_1st
cell_groups = s_groups_IB
cmap = cmap_IB
#initialize figure
height = 15
width = 15
fig = plt.figure(facecolor = 'w', figsize = (width, height))
#define x- and y-limits
x_min, x_max = np.min(tsne['x']), np.max(tsne['x'])
y_min, y_max = np.min(tsne['y']), np.max(tsne['y'])
x_diff, y_diff = x_max - x_min, y_max - y_min
pad = 2.0
if x_diff > y_diff:
xlim = (x_min - 1, x_max + 3)
ylim = (y_min * (x_diff / y_diff) - pad, y_max * (x_diff / y_diff) + pad)
if x_diff < y_diff:
xlim = (x_min * (y_diff/x_diff) - 1, x_max * (y_diff/x_diff) + 3)
ylim = (y_min - pad, y_max + pad)
text_pad = 2
ix_sel = cell_groups.index
#draw groups in background
ax1 = plt.subplot()
ax1.set_xlim(xlim)
ax1.set_ylim(ylim)
remove_ticks(ax1)
ax1.scatter(tsne['x'],
tsne['y'],
s = 150,
linewidth = 0.0,
c = 'silver',
edgecolor = 'silver')
clist_tsne = [cmap[cell_groups[ix]] for ix in ix_sel]
ax1.scatter(tsne.ix[ix_sel, 'x'],
tsne.ix[ix_sel, 'y'],
s = 150,
linewidth = 0.0,
c = clist_tsne,
edgecolors = clist_tsne)
clean_axis(ax1)
figname = 'v1.8_2_D_t-SNE_IB.pdf'
plt.savefig('%s/%s' % (path_figures, figname),
format = 'pdf',
transparent = True,
bbox_inches = 'tight',
pad_inches = 0,
rasterized = True)
In [312]:
#initialize figure
height = 5
width = 5
fig = plt.figure(facecolor = 'w', figsize = (width, height))
#draw inset
ax_inset = plt.subplot()
remove_ticks(ax_inset)
clist_tsne_inset = [cmap_1st[s_groups_1st[ix]] for ix in ix_sel]
ax_inset.scatter(tsne['x'],
tsne['y'],
s = 25,
linewidth = 0.0,
c = 'silver',
cmap = plt.cm.jet,
vmin = 0, vmax = 1.0)
ax_inset.scatter(tsne.ix[ix_sel, 'x'],
tsne.ix[ix_sel, 'y'],
s = 25,
linewidth = 0.0,
c = clist_tsne_inset,
edgecolor = clist_tsne_inset)
for s in ['top','bottom','left','right']:
ax_inset.spines[s].set_linewidth(1)
figname = 'v1.8_S3_A_t-SNE_IB_Inset.pdf'
plt.savefig('%s/%s' % (path_figures, figname),
format = 'pdf',
transparent = True,
bbox_inches = 'tight',
pad_inches = 0,
rasterized = True)
Heatmap¶
In [153]:
#Transfer the expression data to relative values (0 [min] - 1 [max])
seq_sel_IB = log2Transform(seq).ix[g_groups_IB.index, s_groups_IB.index]
seq_sel_IB = seq_sel_IB.apply(lambda x: x / max(x), axis = 1)
In [266]:
dataset = seq_sel_IB
cell_groups = s_groups_IB
gene_groups = g_groups_IB
cmap = cmap_IB
#create figure
bar_size = 0.75 #size of group and metadata bars
bar_pad = 0.1 #pad size of group and metadata bars
width = 0.04 * len(dataset.columns)
height = 32.0
#height = ((width - bar_size) / len(s_groups)) * len(g_groups) + 5 * bar_size
plt.figure(figsize=(width, height), facecolor = 'w')
#generate main axes
axHM = plt.axes()
axHM.set_position([0.00, 0.00, 1.0, 1.0])
#plot heatmap
axHM.matshow(dataset,
cmap = plt.cm.jet,
aspect = 'auto',
interpolation = 'nearest',
vmin = 0,
vmax = 1)
remove_ticks(axHM, linewidth = 1)
clean_axis(axHM)
#create axes divider
divider = make_axes_locatable(axHM)
#draw cell/sample groups
axSG = divider.append_axes("bottom", size= bar_size, pad=bar_pad)
axSG.set_xlim(0, len(cell_groups))
for pos, gr in enumerate(cell_groups):
axSG.axvspan(pos, pos+1, color=cmap[gr])
remove_ticks(axSG, linewidth = 1)
clean_axis(axSG)
#draw Sca1 metadata
axSCA = divider.append_axes("top", size= bar_size, pad=bar_pad)
axSCA.set_xlim(0, len(cell_groups))
for pos, ix in enumerate(cell_groups.index):
axSCA.axvspan(pos, pos+1, color=cmap_Sca1[meta.ix['green_flag', ix]])
remove_ticks(axSCA, linewidth = 1)
clean_axis(axSCA)
#draw hair cycle metadata
axHC = divider.append_axes("top", size= bar_size, pad=bar_pad)
axHC.set_xlim(0, len(cell_groups))
for pos, ix in enumerate(cell_groups.index):
axHC.axvspan(pos, pos+1, color=cmap_hair_cycle[meta.ix['hair_cycle_stage', ix]])
remove_ticks(axHC, linewidth = 1)
clean_axis(axHC)
#draw cell size metadata
axSIZE = divider.append_axes("top", size= bar_size, pad=bar_pad)
axSIZE.set_xlim(0, len(cell_groups))
cmapSIZE = plt.cm.Greens
SIZE_max = meta.ix['diameter(um)', s_groups_1st.index].astype(float).max()
for pos, ix in enumerate(cell_groups.index):
val = float(meta.ix['diameter(um)', ix])
axSIZE.axvspan(pos, pos+1, color=cmapSIZE(val/SIZE_max))
remove_ticks(axSIZE, linewidth = 1)
clean_axis(axSIZE)
#draw transcript metadata
axTRANS = divider.append_axes("top", size= bar_size, pad=bar_pad)
axTRANS.set_xlim(0, len(cell_groups))
cmapTRANS = plt.cm.Reds
TRANS_max = meta.ix['sum_transcripts', s_groups_1st.index].astype(float).max()
for pos, ix in enumerate(cell_groups.index):
val = float(meta.ix['sum_transcripts', ix])
axTRANS.axvspan(pos, pos + 1, color = cmapTRANS(val / TRANS_max))
remove_ticks(axTRANS, linewidth = 1)
clean_axis(axTRANS)
figname = 'v1.8_S3_B_Heatmap_IB.pdf'
plt.savefig('%s/%s' % (path_figures, figname),
format = 'pdf',
transparent = True,
bbox_inches = 'tight',
pad_inches = 0,
rasterized = True)
Barplots¶
In [223]:
genes_IB = ['Krt6a','Krt75','Timp3','Fgf18','Wfdc3','Lima1','Foxp1','S100a6','Postn','Cd34','Krt10','Ptgs1']
In [227]:
genes = genes_IB
cell_groups = s_groups_IB
dataset = seq[s_groups_IB.index]
cmap = cmap_IB
#create figure
bar_size = 0.5
bar_pad = 0.1
width = 10.0
height = len(genes) * 1.5 + (bar_size + bar_pad)
fig = plt.figure(facecolor = 'w', figsize = (width, height))
#create GS for gene barplots
gs1 = plt.GridSpec(len(genes), 1, hspace = 0.00, wspace = 0.0, top = 1, right = 1,
left = (bar_pad + bar_size) / width, bottom = (bar_pad + bar_size) / height)
#create GS for sample group bar
gs0 = plt.GridSpec(1,1, left = (bar_pad + bar_size) / width, right = 1, top = bar_size / height,
bottom = 0, hspace = 0.0, wspace = 0.0)
#colorlist bar
clistBAR = [cmap[val] for val in cell_groups]
#draw genes
for g in genes:
#create group_specific mean expressions
mean_tmp = pd.Series(index = set(cell_groups))
for gr in set(cell_groups):
ix_tmp = cell_groups[cell_groups == gr].index
mean_tmp[gr] = dataset.ix[g, ix_tmp].mean()
#create axis
ax0 = plt.subplot(gs1[genes.index(g)])
#define xaxis
ax0.set_xlim(left = 0, right = (len(dataset.columns)))
ax0.xaxis.set_ticklabels([])
#define yaxis
ax0.set_ylim(bottom = 0, top = np.ceil(np.max(mean_tmp) * 2))
ax0.set_ylabel(g, family = 'Liberation Sans', fontsize = 40, rotation = 'horizontal', va = 'center', ha = 'left')
ax0.yaxis.set_label_coords(1.02, 0.5)
for tick_pos in range(len(ax0.yaxis.get_major_ticks())):
if tick_pos == 0 or tick_pos == len(ax0.yaxis.get_major_ticks()) / 2:
ax0.yaxis.get_major_ticks()[tick_pos].label.set_family('Liberation Sans')
ax0.yaxis.get_major_ticks()[tick_pos].label.set_fontsize(35)
else:
ax0.yaxis.get_major_ticks()[tick_pos].label.set_visible(False)
#draw expression data
ax0.bar(np.arange(0, len(dataset.columns),1), dataset.ix[g], color = clistBAR, linewidth = 0, width = 1.0)
#draw means
ax0.step(range(len(cell_groups)), [mean_tmp[val] for val in cell_groups], where = 'post', color = 'black', linewidth = 2)
ax1 = plt.subplot(gs0[0])
ax1.set_xlim(0, len(cell_groups))
for s_gr in enumerate(cell_groups):
ax1.axvspan(xmin = s_gr[0], xmax = s_gr[0] + 1, color = cmap[s_gr[1]])
remove_ticks(ax1, linewidth = 1)
clean_axis(ax1)
figname = 'v1.8_2_D_Markers_IB.pdf'
plt.savefig('%s/%s' % (path_figures, figname),
format = 'pdf',
transparent = True,
bbox_inches = 'tight',
pad_inches = 0,
rasterized = True)
Markers vs. Baseline¶
In [158]:
genes_baseline_IB = neg_binom_extract_genes(NBR_2nd_size_bl, score = 'percentile - 25', number = 10)
In [275]:
#define input variables
gr_order = return_unique(s_groups_IB)
genes = genes_baseline_IB
L = len(genes.index)
traces = NBR_2nd_traces
cmap = cmap_IB
to_2nd = IB_to_2nd
#initialize figure
height = 5 * len(gr_order)
width = 6
plt.figure(facecolor = 'w', figsize = (width, height))
gs = plt.GridSpec(len(gr_order), 2, hspace = 0.15, wspace = 0.0, width_ratios = [5,8])
#iterate through groups
for pos, gr in enumerate(gr_order):
gr_2nd = to_2nd[gr]
genes_tmp = [gene for gene in genes[str(gr_2nd)].values if gene != None]
#create gene name axis
ax0 = plt.subplot(gs[pos,0])
ax0.set_ylim(L - 0.5, -0.5)
clean_axis(ax0)
#create violin plot axis
ax1 = plt.subplot(gs[pos,1])
ax1.spines['right'].set_color('none')
ax1.set_ylim(L - 0.5, -0.5)
ax1.yaxis.set_ticks_position('left')
ax1.yaxis.set_ticks([])
#iterate over genes
for ix, g in enumerate(genes_tmp):
ax0.text(0.975, ix, g, family = 'Liberation Sans', fontsize = 30, ha = 'right', va = 'center')
#print background
if ix % 2 == 0:
ax1.axhspan(ix - 0.5, ix + 0.5, color = '#F6F6F6', zorder = 0)
#print group traces
violin = ax1.violinplot([x - 1 for x in traces.ix[g, str(gr_2nd)]],
positions = [ix],
vert=False,
showextrema=False)
for v in violin['bodies']:
v.set_edgecolor(cmap[gr])
v.set_facecolor(cmap[gr])
v.set_linewidth(1.0)
v.set_alpha(1.0)
#print baseline traces
violin = ax1.violinplot([x - 1 for x in traces.ix[g, 'Baseline']],
positions = [ix],
vert=False,
showextrema=False)
for v in violin['bodies']:
v.set_edgecolor('#AAAAAA')
v.set_facecolor('#AAAAAA')
v.set_linewidth(1.0)
v.set_alpha(1.0)
for ix, t in enumerate(ax1.xaxis.get_ticklabels()):
if ix % 2 == 0:
t.set_family('Liberation Sans')
t.set_fontsize(25)
else:
t.set_fontsize(0)
figname = 'v1.8_S3_F_Markers_vs_Baseline_IB.pdf'
plt.savefig('%s/%s' % (path_figures, figname),
format = 'pdf',
transparent = True,
bbox_inches = 'tight',
pad_inches = 0,
rasterized = True)
Markers vs. other groups¶
In [68]:
NBR_2nd_bin_gr_IB, NBR_2nd_size_gr_IB = neg_binom_vs_groups_v1P(NBR_2nd_summary[['20','21','22','Baseline']], dview, PP='95')
In [163]:
genes_groups_IB = neg_binom_extract_genes(NBR_2nd_size_gr_IB, score = 'percentile - 25', number = 10)
In [274]:
#define input variables
gr_order = return_unique(s_groups_IB)
genes = genes_groups_IB
L = len(genes.index)
traces = NBR_2nd_traces
summary = NBR_2nd_summary[['20','21','22','Baseline']]
cmap = cmap_IB
to_2nd = IB_to_2nd
#initialize figure
height = 5 * len(gr_order)
width = 6
plt.figure(facecolor = 'w', figsize = (width, height))
gs = plt.GridSpec(len(gr_order), 2, hspace = 0.15, wspace = 0.0, width_ratios = [5,8])
#iterate through groups
for pos, gr in enumerate(gr_order):
gr_2nd = to_2nd[gr]
genes_tmp = [gene for gene in genes[str(gr_2nd)].values if pd.isnull(gene) == False]
#create gene name axis
ax0 = plt.subplot(gs[pos,0])
ax0.set_ylim(L - 0.5, -0.5)
clean_axis(ax0)
#create violin plot axis
ax1 = plt.subplot(gs[pos,1])
ax1.spines['right'].set_color('none')
ax1.set_ylim(L - 0.5, -0.5)
ax1.yaxis.set_ticks_position('left')
ax1.yaxis.set_ticks([])
if len(genes_tmp) == 0:
remove_ticks(ax1)
#iterate over genes
for ix, g in enumerate(genes_tmp):
ax0.text(0.975, ix, g, family = 'Liberation Sans', fontsize = 30, ha = 'right', va = 'center')
#print background
if ix % 2 == 0:
ax1.axhspan(ix - 0.5, ix + 0.5, color = '#F6F6F6', zorder = 0)
#print group traces
violin = ax1.violinplot([x - 1 for x in traces.ix[g, str(gr_2nd)]],
positions = [ix],
vert=False,
showextrema=False)
for v in violin['bodies']:
v.set_edgecolor(cmap[gr])
v.set_facecolor(cmap[gr])
v.set_linewidth(1.0)
v.set_alpha(1.0)
#print 2nd group traces
gr_sec = summary.ix[g].ix['mean'].order().index[-2]
violin = ax1.violinplot([x - 1 for x in traces.ix[g, gr_sec]],
positions = [ix],
vert=False,
showextrema=False)
for v in violin['bodies']:
v.set_edgecolor('#AAAAAA')
v.set_facecolor('#AAAAAA')
v.set_linewidth(1.0)
v.set_alpha(1.0)
for ix, t in enumerate(ax1.xaxis.get_ticklabels()):
if ix % 2 == 0:
t.set_family('Liberation Sans')
t.set_fontsize(25)
else:
t.set_fontsize(0)
figname = 'v1.8_S3_F_Markers_vs_Groups_IB.pdf'
plt.savefig('%s/%s' % (path_figures, figname),
format = 'pdf',
transparent = True,
bbox_inches = 'tight',
pad_inches = 0,
rasterized = True)
Robustness¶
In [292]:
robustness = robustness_IB
robustness_null = robustness_IB_null
cell_groups = s_groups_IB
cmap = cmap_IB
#generate figure
fig = plt.figure(facecolor = 'w', figsize = (14, 8))
gs = plt.GridSpec(nrows = 2, ncols = 1, wspace=0.0, hspace=0.0, height_ratios=[1.0, 11.0])
#generate plot
ax = plt.subplot(gs[1])
#generate xaxis
ax.set_xlim(- 0.5, len(set(cell_groups)) - 0.5)
ax.set_xticks(range(len(set(cell_groups))))
ax.set_xticklabels(np.arange(0, len(set(cell_groups))) + 1, family = 'Liberation Sans', fontsize = 35)
ax.set_xlabel('Affinity propagation cluster #', family = 'Liberation Sans', fontsize = 40)
ax.xaxis.set_label_coords(0.5, -0.1)
ax.tick_params(axis='x', which='major', pad=10)
#generate yaxis
ax.set_ylim(0, 1)
ax.set_yticks(np.arange(0, 1.1, 0.1))
ax.set_yticklabels([0.0,'',0.2,'',0.4,'',0.6,'',0.8,'',1.0], family = 'Liberation Sans', fontsize = 35)
ax.set_ylabel('Fraction of cell retained', family = 'Liberation Sans', fontsize = 40)
ax.yaxis.set_label_coords(-0.075, 0.5)
ax.tick_params(axis='y', which='major', pad=10)
#plot data
for ix, gr in enumerate(return_unique(cell_groups)):
ax.scatter([ix - (np.random.random() * 0.4 - 0.2) for pos in robustness[str(gr)]],
robustness[str(gr)],
s = 50,
color = 'blue')
ax.plot([ix - 0.25, ix + 0.25],
[np.mean(robustness[str(gr)])] * 2,
linewidth = 5,
color = 'black')
ax.scatter([ix - (np.random.random() * 0.4 - 0.2) for pos in robustness[str(gr)]],
robustness_null[str(gr)],
s = 50,
color = 'red')
ax.plot([ix - 0.25, ix + 0.25],
[np.mean(robustness_null[str(gr)])] * 2,
linewidth = 5,
color = 'black')
#plot color symbols
ax_Sym = plt.subplot(gs[0])
ax_Sym.set_xlim(- 0.5, len(set(cell_groups)) - 0.5)
ax_Sym.set_ylim(-0.5, 0.5)
for ix, gr in enumerate(return_unique(cell_groups)):
ax_Sym.scatter(ix,
0.0,
marker = 'H',
s = 750,
color = cmap[gr])
clean_axis(ax_Sym)
figname = 'v1.8_S3_J_Robustness_IB.pdf'
plt.savefig('%s/%s' % (path_figures, figname),
format = 'pdf',
transparent = True,
bbox_inches = 'tight',
pad_inches = 0,
rasterized = True)
In [230]:
cell_groups = s_groups_IB
group_pos = [(0.5,0),(0.5,1),(0.5,2),(5.5,0),(5.5,1),(5.5,2)]
cmap = cmap_IB
nmap = nmap_IB
nmap_genes = gmap_IB
#initialize figure
width = 16
height = 8
fig = plt.figure(facecolor = 'w', figsize = (width, height))
ax0 = plt.subplot()
ax0.set_ylim(2.75, -0.25)
ax0.set_xlim(0, 10)
group_list = return_unique(cell_groups)
for pos, gr in enumerate(group_list):
pos_tmp = group_pos[pos]
ax0.scatter(pos_tmp[0], pos_tmp[1], c = cmap[gr], marker = 'H', vmin = 0, vmax = 1.0, s = 750,
linewidth = 0.0, edgecolor = cmap[gr])
ax0.text(pos_tmp[0] + 0.5, pos_tmp[1], nmap[gr], family = 'Liberation Sans',
fontsize = 40, va = 'center', ha = 'left')
ax0.text(pos_tmp[0] + 0.5, pos_tmp[1] + 0.5, nmap_genes[group_list[pos]],
fontsize = 30, family = 'Liberation Sans', va = 'center', ha = 'left',
fontstyle = 'italic')
#clean_axis(ax0)
figname = 'v1.8_2_D_Legend_IB.pdf'
plt.savefig('%s/%s' % (path_figures, figname),
format = 'pdf',
transparent = True,
bbox_inches = 'tight',
pad_inches = 0,
rasterized = True)
Wards linkage¶
In [49]:
seq = loadData_v1(path_input, exp_id, 'seq', 'DataFrame')
In [50]:
seq_sel = dropNull(seq[s_groups_2nd[s_groups_2nd.isin([0,1,2,7,8,9,10,11,12,13,15,16,17,18,19,20,21,22])].index], path_input, drop_spikes=True, drop_repeats=True, cutoff_mean=0.05)
In [51]:
seq_sel = log2Transform(seq_sel)
In [52]:
groups_sel = [0,1,2,7,8,9,10,11,12,13,15,16,17,18,19,20,21,22]
#calculate group averages
group_avg = pd.DataFrame(index = seq_sel.index, columns = set(groups_sel))
for gr in groups_sel:
ix_tmp = s_groups_2nd[s_groups_2nd==gr].index
group_avg_tmp = seq_sel[ix_tmp].mean(axis=1)
group_avg[gr] = group_avg_tmp
In [53]:
#create group distance matrix
group_avg_dist = 1 - group_avg.corr()
In [54]:
#create single-linkage clustering
seq_cluster = scipy.cluster.hierarchy.ward(group_avg_dist)
In [55]:
cmap = cmap_2nd
nmap_short = nmap_2nd_short
#create figure and GridSpec
height = 12
width = 20
fig = plt.figure(facecolor = 'w', figsize = (width, height))
heatmapGS = plt.GridSpec(1,3,wspace=0.0,hspace=0.0,width_ratios=[4,12,4])
#create dendogramm
ax_dend = fig.add_subplot(heatmapGS[0])
den = sch.dendrogram(seq_cluster,color_threshold=np.inf, orientation='right')
leaves = den['leaves']
lines = ax_dend.get_children()[2]
lines.set_color('black')
lines.set_linewidth(2)
clean_axis(ax_dend)
#create heatmap
ax_hm = fig.add_subplot(heatmapGS[1])
ordered = [group_avg_dist.columns[ix] for ix in leaves]
data_hm = group_avg_dist.ix[ordered, ordered]
ax_hm.matshow(data_hm,interpolation='nearest',aspect='auto',origin='lower',cmap=plt.cm.seismic, vmin = 0.0, vmax = 0.35)
remove_ticks(ax_hm)
clean_axis(ax_hm)
#create labels
ax_label = fig.add_subplot(heatmapGS[2])
ax_label.set_ylim(bottom = 0, top = len(leaves))
ax_label.set_xlim(left = 0, right = 10)
for pos, ix in enumerate(leaves):
ax_label.scatter(x = 1.5,
y = pos + 0.5,
c = cmap[group_avg_dist.columns[ix]],
linewidth = 0.0,
s = markers_2nd_size[group_avg_dist.columns[ix]],
marker = markers_2nd[group_avg_dist.columns[ix]])
ax_label.text(x = 3,
y = pos + 0.5,
s = nmap_short[group_avg_dist.columns[ix]],
family = 'Liberation Sans',
size = 35,
va = 'center')
clean_axis(ax_label)
"""
figname = 'v1.7_S7_H_Wards_linkage.pdf'
plt.savefig('%s/%s' % (path_figures, figname),
format = 'pdf',
transparent = True,
bbox_inches = 'tight',
pad_inches = 0,
rasterized = True)
"""
Out[55]:
In [56]:
cmap = cmap_2nd
nmap_short = nmap_2nd_short
#create figure and GridSpec
height = 20
width = 12
fig = plt.figure(facecolor = 'w', figsize = (width, height))
heatmapGS = plt.GridSpec(3,1,wspace=0.0,hspace=0.0,height_ratios=[4,12,4])
#create dendogramm
ax_dend = fig.add_subplot(heatmapGS[0])
den = sch.dendrogram(seq_cluster,color_threshold=np.inf, orientation='top')
leaves = den['leaves']
lines = ax_dend.get_children()[2]
lines.set_color('black')
lines.set_linewidth(2)
clean_axis(ax_dend)
#create heatmap
ax_hm = fig.add_subplot(heatmapGS[1])
ordered = [group_avg_dist.columns[ix] for ix in leaves]
data_hm = group_avg_dist.ix[ordered, ordered]
ax_hm.matshow(data_hm,interpolation='nearest',aspect='auto',origin='lower',cmap=plt.cm.seismic, vmin = 0.0, vmax = 0.35)
remove_ticks(ax_hm)
clean_axis(ax_hm)
#create labels
ax_label = fig.add_subplot(heatmapGS[2])
ax_label.set_xlim(0, len(leaves))
ax_label.set_ylim(10, 0)
for pos, ix in enumerate(leaves):
ax_label.scatter(y = 1.5,
x = pos + 0.5,
c = cmap[group_avg_dist.columns[ix]],
linewidth = 0.0,
s = markers_2nd_size[group_avg_dist.columns[ix]],
marker = markers_2nd[group_avg_dist.columns[ix]])
ax_label.text(y = 3,
x = pos + 0.5,
s = nmap_short[group_avg_dist.columns[ix]],
family = 'Liberation Sans',
size = 35,
va = 'top', ha = 'center', rotation = 'vertical')
clean_axis(ax_label)
figname = 'v1.8_S3_K_2nd_level_Wards_linkage.pdf'
plt.savefig('%s/%s' % (path_figures, figname),
format = 'pdf',
transparent = True,
bbox_inches = 'tight',
pad_inches = 0,
rasterized = True)
In [302]:
cmap = plt.cm.seismic_r
#initialize figure
height = 1
width = 9
fig = plt.figure(facecolor = 'w', figsize = (width, height))
#draw
axLabel = plt.subplot()
axLabel.matshow(np.matrix(np.arange(0.0, 1.0, 0.001)),
cmap = cmap, aspect = 'auto', vmin = 0.0, vmax = 1.0)
clean_axis(axLabel)
axLabel.yaxis.set_ticks([])
axLabel.xaxis.set_ticks_position('bottom')
axLabel.set_xticks([axLabel.get_xlim()[0], axLabel.get_xlim()[1]])
axLabel.set_xticklabels(['0.65','1.0'], family = 'Liberation Sans', fontsize = 35, va = 'center')
plt.rcParams['xtick.major.pad'] = 20
axLabel.set_xlabel('Pearson correlation', family = 'Liberation Sans', fontsize = 40)
axLabel.xaxis.set_label_coords(0.5, -0.6)
figname = 'v1.8_S3_K_Legend_correlation.pdf'
plt.savefig('%s/%s' % (path_figures, figname),
format = 'pdf',
transparent = True,
bbox_inches = 'tight',
pad_inches = 0,
rasterized = True)
Combined legends¶
In [31]:
nmap_2nd = {0:'Interfollicular basal I',
1:'Interfollicular basal II',
2:'Infundibular basal',
3:'IFE differentiated cells I',
4:'IFE differentiated cells II',
5:'IFE keratinized layer I',
6:'IFE keratinized layer II',
7:'upper HF I',
8:'upper HF II',
9:'upper HF III',
10:'upper HF IV',
11:'upper HF V',
12:'upper HF VI',
13:'upper HF VII',
14:'Sebaceous gland (SG)',
15:'Outer bulge I',
16:'Outer bulge II',
17:'Outer bulge III',
18:'Outer bulge IV',
19:'Outer bulge V',
20:'Inner bulge I',
21:'Inner bulge II',
22:'Inner bulge III',
23:'T cells (TC)',
24:'Langerhans cells (LH)'}
In [32]:
gmap_2nd = {0:'Krt14(hi), Krt5(hi), Mt2(hi), Fth1(hi),\nThbs1(hi)',
1:'Krt14(dim), Krt5(dim), Mt2(dim), Fth1(dim),\nThbs1(lo)',
2:'Krt14(dim), Krt5(dim), Mt2(dim), Fth1(dim),\nSostdc1(hi), Aqp3(hi), Fst(hi), Postn(dim)',
3:'Krt10(dim), Ptgs1(dim)',
4:'Krt10(hi), Ptgs1(hi)',
5:'Lor(dim), Flg2(dim)',
6:'Lor(hi), Flg2(hi)',
7:'Krt79(hi), Krt17(hi), Defb6(hi),\nCst6(hi), Klk10(hi), Cryab(hi)',
8:'Krt79(hi), Krt17(hi), Defb6(hi), Cst6(dim)\nKrt14(dim), Lrig1(+), Krt5(lo), Ptn(lo)',
9:'Krt79(dim), Krt17(dim), Defb6(lo), Cst6(lo),\nKrt14(dim), Lrig1(+), Krt5(lo), Ptn(lo)',
10:'Krt79(lo), Krt17(lo), Krt14(dim), Lrig1(+)\nKrt5(dim), Ptn(dim)',
11:'Krt79(hi), Krt17(hi), Krt14(dim), Krt5(dim),\nKrt10(dim), Ptgs1(dim)',
12:'Krt79(hi), Krt17(hi), Krt10(hi), Ptgs1(hi)',
13:'Krt79(hi), Krt17(hi),\nLor(hi), Flg2(hi)',
14:'Mgst1(hi), Scd1(hi)',
15:'Postn(hi), Cd34(hi), Lgr5(hi), Krt24(hi),\nSfrp1(hi)',
16:'Postn(hi), Cd34(hi), Lgr5(dim), Krt24(dim),\nSfrp1(dim)',
17:'Postn(hi), Cd34(hi), Aspn(hi), Nrep(hi),\nKrt17(dim), Krt15(lo)',
18:'Postn(hi), Cd34(hi), Krt79(dim), Krt17(dim)',
19:'Postn(hi), Cd34(hi), Krt10(hi), Ptgs1(hi)',
20:'Krt6a(hi), Krt75(hi)',
21:'Krt6a(hi), Krt75(hi), Postn(hi), Cd34(hi)',
22:'Krt6a(hi), Krt75(hi), Krt10(hi), Ptgs1(hi)',
23:'Cd3(hi), Thy1(hi)',
24:'Cd207(hi), Ctss(hi)'}
In [48]:
#initialize figure
width = 17
height = 23
fig = plt.figure(facecolor = 'w', figsize = (width, height))
gs = plt.GridSpec(1,1)
group_list = [0,1,2,7,8,9,10,11,12,13,15,16,17,18,19,20,21,22]
ax0 = fig.add_subplot(gs[0])
ax0.set_ylim(len(group_list) - 0.5, -0.5)
ax0.set_xlim(0, 1)
x = 0.03
for pos, gr in enumerate(group_list):
ax0.scatter(x, pos - 0.05,
c = cmap_2nd[gr],
marker = markers_2nd[gr],
s = markers_2nd_size[gr],
linewidth = 0.0)
ax0.text(x + 0.03, pos, nmap_2nd[gr], family = 'Liberation Sans', fontsize = 40, va = 'center', ha = 'left')
ax0.text(x + 0.45, pos, gmap_2nd[gr], fontsize = 25, family = 'Liberation Sans', va = 'center', ha = 'left',
fontstyle = 'italic')
clean_axis(ax0)
figname = 'v1.8_2_Legend_groups.pdf'
plt.savefig('%s/%s' % (path_figures, figname),
format = 'pdf',
transparent = True,
bbox_inches = 'tight',
pad_inches = 0,
rasterized = True)
In [ ]:
#initialize figure
width = 17
height = 23
fig = plt.figure(facecolor = 'w', figsize = (width, height))
gs = plt.GridSpec(1,1)
group_list = [0,1,2,7,8,9,10,11,12,13,15,16,17,18,19,20,21,22]
ax0 = fig.add_subplot(gs[0])
ax0.set_ylim(len(group_list) - 0.5, -0.5)
ax0.set_xlim(0, 1)
x = 0.03
for pos, gr in enumerate(group_list):
ax0.scatter(x, pos - 0.05,
c = cmap_2nd[gr],
marker = markers_2nd[gr],
s = markers_2nd_size[gr],
linewidth = 0.0)
ax0.text(x + 0.05, pos, nmap_2nd_short[gr], family = 'Liberation Sans', fontsize = 40, va = 'center', ha = 'left')
ax0.text(x + 0.3, pos, gmap_2nd[gr], fontsize = 25, family = 'Liberation Sans', va = 'center', ha = 'left',
fontstyle = 'italic')
clean_axis(ax0)
figname = 'v1.8_2_Legend_groups.pdf'
plt.savefig('%s/%s' % (path_figures, figname),
format = 'pdf',
transparent = True,
bbox_inches = 'tight',
pad_inches = 0,
rasterized = True)
t-SNE with all keratinocytes marked¶
In [313]:
#initialize figure
height = 5
width = 5
fig = plt.figure(facecolor = 'w', figsize = (width, height))
#draw inset
ax_inset = plt.subplot()
remove_ticks(ax_inset)
ix_sel = s_groups_1st[~s_groups_1st.isin([5,11,12])].index
clist_tsne_inset = [cmap_1st[s_groups_1st[ix]] for ix in ix_sel]
ax_inset.scatter(tsne['x'],
tsne['y'],
s = 25,
linewidth = 0.0,
c = 'silver',
cmap = plt.cm.jet,
vmin = 0, vmax = 1.0)
ax_inset.scatter(tsne.ix[ix_sel, 'x'],
tsne.ix[ix_sel, 'y'],
s = 25,
linewidth = 0.0,
c = clist_tsne_inset,
edgecolor = clist_tsne_inset)
for s in ['top','bottom','left','right']:
ax_inset.spines[s].set_linewidth(1)
figname = 'v1.8_S3_A_t-SNE_kerats_Inset.pdf'
plt.savefig('%s/%s' % (path_figures, figname),
format = 'pdf',
transparent = True,
bbox_inches = 'tight',
pad_inches = 0,
rasterized = True)
Save 2nd level neg. binominal regression data to txt file¶
In [62]:
seq_ = dropNull(seq, path_input, cutoff_mean = 0.00)
In [64]:
groups_sel = [0,1,2,7,8,9,10,11,12,13,15,16,17,18,19,20,21,22]
In [77]:
m_ix1, m_ix2 = [],[]
for gr in groups_sel:
m_ix1 += [nmap_2nd_short[gr]] * 2
m_ix2 += ['vs Baseline','vs other groups']
NBR_2nd_excel = pd.DataFrame(index = seq_.index, columns = [np.array(m_ix1), np.array(m_ix2)])
ix_sel = NBR_2nd_size_bl.index.levels[0]
for gr in groups_sel:
NBR_2nd_excel[nmap_2nd_short[gr], 'vs Baseline'].ix[ix_sel] = NBR_2nd_size_bl.swaplevel(0,1).ix['percentile - 25'].ix[ix_sel][str(gr)]
subsets = [[0,1,2, NBR_2nd_size_gr_IFE_b],
[7,8,9,10,11,12,13,NBR_2nd_size_gr_uHF],
[15,16,17,18,19,NBR_2nd_size_gr_OB],
[20,21,22,NBR_2nd_size_gr_IB]]
for s in subsets:
data_tmp = s[-1]
for gr in s[:-1]:
NBR_2nd_excel[nmap_2nd_short[gr], 'vs other groups'].ix[ix_sel] = data_tmp.swaplevel(0,1).ix['percentile - 25'].ix[ix_sel][str(gr)]
NBR_2nd_excel[NBR_2nd_excel=='n.s'] = 'n.s.'
NBR_2nd_excel = NBR_2nd_excel.fillna('b.c.')
In [89]:
#saveData_v1(NBR_2nd_excel, path_output, exp_id, 'NBR_2nd_excel')
n = 19 experiment¶
In [236]:
def get_random_no_mice(ix, cells_all, meta, no_mice_emp, repeats = 1000):
#get Sca1+ and Sca1- mouse pools for sampling
pool_Sca1_pos, pool_Sca1_neg = [],[]
for c in cells_all.index:
if meta.ix['cell_fraction', c] == 'Sca1+':
pool_Sca1_pos += [meta.ix['mouse_number',c]]
elif meta.ix['cell_fraction', c] == 'Sca1-':
pool_Sca1_neg += [meta.ix['mouse_number',c]]
#get cells for ix
ix_sel = cells_all[cells_all==ix].index
#get no of Sca1+ and Sca1- cells for ix
tmp = Counter(meta.ix['cell_fraction', ix_sel])
no_Sca1_pos, no_Sca1_neg = tmp['Sca1+'], tmp['Sca1-']
#define output
no_mice = []
#iterate over repeats
for r in range(repeats):
#sample mice from Sca1+, Sca1- pools according to number of Sca1+, Sca1- cells
tmp = list(np.random.choice(pool_Sca1_pos, no_Sca1_pos)) + list(np.random.choice(pool_Sca1_neg, no_Sca1_neg))
no_mice += [len(set(tmp))]
#define average
avg = np.mean(no_mice)
#define one-sided p-val (probability that less mice make up group by chance compared to empirical value)
pval = np.sum([val <= no_mice_emp for val in no_mice]) / float(len(no_mice))
return avg, pval
In [237]:
#define DataFrame
n19 = pd.DataFrame(index = [nmap_2nd_short[ix] for ix in return_unique(s_groups_2nd)],
columns = ['Sca1+ fraction','Number of cells','Number of mice','Number of mice if random','P-value'])
for ix in return_unique(s_groups_2nd):
#put in data
ix_sel = s_groups_2nd[s_groups_2nd==ix].index
len_tmp = float(len(ix_sel))
#% of Sca1+ fraction
n19.ix[ix,'Sca1+ fraction'] = '%.1f %%' % (Counter(meta.ix['cell_fraction', ix_sel])['Sca1+'] / len_tmp * 100)
#number of cells
n19.ix[ix, 'Number of cells'] = '%s / %s' % (int(len_tmp), len(s_groups_2nd))
#number of mice
no_mice = len(set(meta.ix['mouse_number', ix_sel]))
n19.ix[ix, 'Number of mice'] = '%s / %s' % (no_mice, len(set(meta.ix['mouse_number', s_groups_2nd.index])))
#simulate randomly expected number of mice
tmp = get_random_no_mice(ix, s_groups_2nd, meta, no_mice, repeats = 10000)
n19.ix[ix, 'Number of mice if random'], n19.ix[ix, 'P-value'] = tmp[0], tmp[1]
In [238]:
n19
Out[238]:
n = 19 visualization¶
In [243]:
mice = list(set(meta.ix['mouse_number', s_groups_2nd.index]))
mice.sort()
In [265]:
fig = plt.figure(facecolor = 'w', figsize = (20, 25))
tsne = tsne_1st
#define x- and y-limits
x_min, x_max = np.min(tsne['x']), np.max(tsne['x'])
y_min, y_max = np.min(tsne['y']), np.max(tsne['y'])
x_diff, y_diff = x_max - x_min, y_max - y_min
pad = 2.0
if x_diff > y_diff:
xlim = (x_min - 1, x_max + 3)
ylim = (y_min * (x_diff / y_diff) - pad, y_max * (x_diff / y_diff) + pad)
if x_diff < y_diff:
xlim = (x_min * (y_diff/x_diff) - 1, x_max * (y_diff/x_diff) + 3)
ylim = (y_min - pad, y_max + pad)
text_pad = 2
for ix, m in enumerate(mice):
ax = plt.subplot(5,4, ix + 1)
ix_sel = meta.ix['mouse_number', s_groups_2nd.index][meta.ix['mouse_number', s_groups_2nd.index]==m].index
ax.scatter(tsne['x'],
tsne['y'],
s = 25,
linewidth = 0.0,
c = 'silver',
cmap = plt.cm.jet,
vmin = 0, vmax = 1.0)
ax.scatter(tsne.ix[ix_sel, 'x'],
tsne.ix[ix_sel, 'y'],
s = 30,
linewidth = 0.0,
c = 'red',
edgecolor = 'red')
ax.text(ax.get_xlim()[0],
ax.get_ylim()[1],
m,
family ='Liberation Sans', fontsize = 30)
clean_axis(ax)
fig.tight_layout()
